data engineer case study interview

Data Science Case Study Interview: Your Guide to Success

by Sam McKay, CFA | Careers

Ready to crush your next data science interview? Well, you’re in the right place.

This type of interview is designed to assess your problem-solving skills, technical knowledge, and ability to apply data-driven solutions to real-world challenges.

So, how can you master these interviews and secure your next job?

To master your data science case study interview:

Practice Case Studies: Engage in mock scenarios to sharpen problem-solving skills.

Review Core Concepts: Brush up on algorithms, statistical analysis, and key programming languages.

Contextualize Solutions: Connect findings to business objectives for meaningful insights.

Clear Communication: Present results logically and effectively using visuals and simple language.

Adaptability and Clarity: Stay flexible and articulate your thought process during problem-solving.

This article will delve into each of these points and give you additional tips and practice questions to get you ready to crush your upcoming interview!

After you’ve read this article, you can enter the interview ready to showcase your expertise and win your dream role.

Let’s dive in!

Table of Contents

What to Expect in the Interview?

Data science case study interviews are an essential part of the hiring process. They give interviewers a glimpse of how you, approach real-world business problems and demonstrate your analytical thinking, problem-solving, and technical skills.

Furthermore, case study interviews are typically open-ended , which means you’ll be presented with a problem that doesn’t have a right or wrong answer.

Instead, you are expected to demonstrate your ability to:

Break down complex problems

Make assumptions

Gather context

Provide data points and analysis

This type of interview allows your potential employer to evaluate your creativity, technical knowledge, and attention to detail.

But what topics will the interview touch on?

Topics Covered in Data Science Case Study Interviews

In a case study interview , you can expect inquiries that cover a spectrum of topics crucial to evaluating your skill set:

Topic 1: Problem-Solving Scenarios

In these interviews, your ability to resolve genuine business dilemmas using data-driven methods is essential.

These scenarios reflect authentic challenges, demanding analytical insight, decision-making, and problem-solving skills.

Real-world Challenges: Expect scenarios like optimizing marketing strategies, predicting customer behavior, or enhancing operational efficiency through data-driven solutions.

Analytical Thinking: Demonstrate your capacity to break down complex problems systematically, extracting actionable insights from intricate issues.

Decision-making Skills: Showcase your ability to make informed decisions, emphasizing instances where your data-driven choices optimized processes or led to strategic recommendations.

Your adeptness at leveraging data for insights, analytical thinking, and informed decision-making defines your capability to provide practical solutions in real-world business contexts.

Topic 2: Data Handling and Analysis

Data science case studies assess your proficiency in data preprocessing, cleaning, and deriving insights from raw data.

Data Collection and Manipulation: Prepare for data engineering questions involving data collection, handling missing values, cleaning inaccuracies, and transforming data for analysis.

Handling Missing Values and Cleaning Data: Showcase your skills in managing missing values and ensuring data quality through cleaning techniques.

Data Transformation and Feature Engineering: Highlight your expertise in transforming raw data into usable formats and creating meaningful features for analysis.

Mastering data preprocessing—managing, cleaning, and transforming raw data—is fundamental. Your proficiency in these techniques showcases your ability to derive valuable insights essential for data-driven solutions.

Topic 3: Modeling and Feature Selection

Data science case interviews prioritize your understanding of modeling and feature selection strategies.

Model Selection and Application: Highlight your prowess in choosing appropriate models, explaining your rationale, and showcasing implementation skills.

Feature Selection Techniques: Understand the importance of selecting relevant variables and methods, such as correlation coefficients, to enhance model accuracy.

Ensuring Robustness through Random Sampling: Consider techniques like random sampling to bolster model robustness and generalization abilities.

Excel in modeling and feature selection by understanding contexts, optimizing model performance, and employing robust evaluation strategies.

Become a master at data modeling using these best practices:

Topic 4: Statistical and Machine Learning Approach

These interviews require proficiency in statistical and machine learning methods for diverse problem-solving. This topic is significant for anyone applying for a machine learning engineer position.

Using Statistical Models: Utilize logistic and linear regression models for effective classification and prediction tasks.

Leveraging Machine Learning Algorithms: Employ models such as support vector machines (SVM), k-nearest neighbors (k-NN), and decision trees for complex pattern recognition and classification.

Exploring Deep Learning Techniques: Consider neural networks, convolutional neural networks (CNN), and recurrent neural networks (RNN) for intricate data patterns.

Experimentation and Model Selection: Experiment with various algorithms to identify the most suitable approach for specific contexts.

Combining statistical and machine learning expertise equips you to systematically tackle varied data challenges, ensuring readiness for case studies and beyond.

Topic 5: Evaluation Metrics and Validation

In data science interviews, understanding evaluation metrics and validation techniques is critical to measuring how well machine learning models perform.

Choosing the Right Metrics: Select metrics like precision, recall (for classification), or R² (for regression) based on the problem type. Picking the right metric defines how you interpret your model’s performance.

Validating Model Accuracy: Use methods like cross-validation and holdout validation to test your model across different data portions. These methods prevent errors from overfitting and provide a more accurate performance measure.

Importance of Statistical Significance: Evaluate if your model’s performance is due to actual prediction or random chance. Techniques like hypothesis testing and confidence intervals help determine this probability accurately.

Interpreting Results: Be ready to explain model outcomes, spot patterns, and suggest actions based on your analysis. Translating data insights into actionable strategies showcases your skill.

Finally, focusing on suitable metrics, using validation methods, understanding statistical significance, and deriving actionable insights from data underline your ability to evaluate model performance.

Also, being well-versed in these topics and having hands-on experience through practice scenarios can significantly enhance your performance in these case study interviews.

Prepare to demonstrate technical expertise and adaptability, problem-solving, and communication skills to excel in these assessments.

Now, let’s talk about how to navigate the interview.

Here is a step-by-step guide to get you through the process.

Steps by Step Guide Through the Interview

This section’ll discuss what you can expect during the interview process and how to approach case study questions.

Step 1: Problem Statement: You’ll be presented with a problem or scenario—either a hypothetical situation or a real-world challenge—emphasizing the need for data-driven solutions within data science.

Step 2: Clarification and Context: Seek more profound clarity by actively engaging with the interviewer. Ask pertinent questions to thoroughly understand the objectives, constraints, and nuanced aspects of the problem statement.

Step 3: State your Assumptions: When crucial information is lacking, make reasonable assumptions to proceed with your final solution. Explain these assumptions to your interviewer to ensure transparency in your decision-making process.

Step 4: Gather Context: Consider the broader business landscape surrounding the problem. Factor in external influences such as market trends, customer behaviors, or competitor actions that might impact your solution.

Step 5: Data Exploration: Delve into the provided datasets meticulously. Cleanse, visualize, and analyze the data to derive meaningful and actionable insights crucial for problem-solving.

Step 6: Modeling and Analysis: Leverage statistical or machine learning techniques to address the problem effectively. Implement suitable models to derive insights and solutions aligning with the identified objectives.

Step 7: Results Interpretation: Interpret your findings thoughtfully. Identify patterns, trends, or correlations within the data and present clear, data-backed recommendations relevant to the problem statement.

Step 8: Results Presentation: Effectively articulate your approach, methodologies, and choices coherently. This step is vital, especially when conveying complex technical concepts to non-technical stakeholders.

Remember to remain adaptable and flexible throughout the process and be prepared to adapt your approach to each situation.

Now that you have a guide on navigating the interview, let us give you some tips to help you stand out from the crowd.

Top 3 Tips to Master Your Data Science Case Study Interview

Approaching case study interviews in data science requires a blend of technical proficiency and a holistic understanding of business implications.

Here are practical strategies and structured approaches to prepare effectively for these interviews:

1. Comprehensive Preparation Tips

To excel in case study interviews, a blend of technical competence and strategic preparation is key.

Here are concise yet powerful tips to equip yourself for success:

Practice with Mock Case Studies : Familiarize yourself with the process through practice. Online resources offer example questions and solutions, enhancing familiarity and boosting confidence.

Review Your Data Science Toolbox: Ensure a strong foundation in fundamentals like data wrangling, visualization, and machine learning algorithms. Comfort with relevant programming languages is essential.

Simplicity in Problem-solving: Opt for clear and straightforward problem-solving approaches. While advanced techniques can be impressive, interviewers value efficiency and clarity.

Interviewers also highly value someone with great communication skills. Here are some tips to highlight your skills in this area.

2. Communication and Presentation of Results

In case study interviews, communication is vital. Present your findings in a clear, engaging way that connects with the business context. Tips include:

Contextualize results: Relate findings to the initial problem, highlighting key insights for business strategy.

Use visuals: Charts, graphs, or diagrams help convey findings more effectively.

Logical sequence: Structure your presentation for easy understanding, starting with an overview and progressing to specifics.

Simplify ideas: Break down complex concepts into simpler segments using examples or analogies.

Mastering these techniques helps you communicate insights clearly and confidently, setting you apart in interviews.

Lastly here are some preparation strategies to employ before you walk into the interview room.

3. Structured Preparation Strategy

Prepare meticulously for data science case study interviews by following a structured strategy.

Here’s how:

Practice Regularly: Engage in mock interviews and case studies to enhance critical thinking and familiarity with the interview process. This builds confidence and sharpens problem-solving skills under pressure.

Thorough Review of Concepts: Revisit essential data science concepts and tools, focusing on machine learning algorithms, statistical analysis, and relevant programming languages (Python, R, SQL) for confident handling of technical questions.

Strategic Planning: Develop a structured framework for approaching case study problems. Outline the steps and tools/techniques to deploy, ensuring an organized and systematic interview approach.

Understanding the Context: Analyze business scenarios to identify objectives, variables, and data sources essential for insightful analysis.

Ask for Clarification: Engage with interviewers to clarify any unclear aspects of the case study questions. For example, you may ask ‘What is the business objective?’ This exhibits thoughtfulness and aids in better understanding the problem.

Transparent Problem-solving: Clearly communicate your thought process and reasoning during problem-solving. This showcases analytical skills and approaches to data-driven solutions.

Blend technical skills with business context, communicate clearly, and prepare to systematically ace your case study interviews.

Now, let’s really make this specific.

Each company is different and may need slightly different skills and specializations from data scientists.

However, here is some of what you can expect in a case study interview with some industry giants.

Case Interviews at Top Tech Companies

As you prepare for data science interviews, it’s essential to be aware of the case study interview format utilized by top tech companies.

In this section, we’ll explore case interviews at Facebook, Twitter, and Amazon, and provide insight into what they expect from their data scientists.

Facebook predominantly looks for candidates with strong analytical and problem-solving skills. The case study interviews here usually revolve around assessing the impact of a new feature, analyzing monthly active users, or measuring the effectiveness of a product change.

To excel during a Facebook case interview, you should break down complex problems, formulate a structured approach, and communicate your thought process clearly.

Twitter , similar to Facebook, evaluates your ability to analyze and interpret large datasets to solve business problems. During a Twitter case study interview, you might be asked to analyze user engagement, develop recommendations for increasing ad revenue, or identify trends in user growth.

Be prepared to work with different analytics tools and showcase your knowledge of relevant statistical concepts.

Amazon is known for its customer-centric approach and data-driven decision-making. In Amazon’s case interviews, you may be tasked with optimizing customer experience, analyzing sales trends, or improving the efficiency of a certain process.

Keep in mind Amazon’s leadership principles, especially “Customer Obsession” and “Dive Deep,” as you navigate through the case study.

Remember, practice is key. Familiarize yourself with various case study scenarios and hone your data science skills.

With all this knowledge, it’s time to practice with the following practice questions.

Mockup Case Studies and Practice Questions

To better prepare for your data science case study interviews, it’s important to practice with some mockup case studies and questions.

One way to practice is by finding typical case study questions.

Here are a few examples to help you get started:

Customer Segmentation: You have access to a dataset containing customer information, such as demographics and purchase behavior. Your task is to segment the customers into groups that share similar characteristics. How would you approach this problem, and what machine-learning techniques would you consider?

Fraud Detection: Imagine your company processes online transactions. You are asked to develop a model that can identify potentially fraudulent activities. How would you approach the problem and which features would you consider using to build your model? What are the trade-offs between false positives and false negatives?

Demand Forecasting: Your company needs to predict future demand for a particular product. What factors should be taken into account, and how would you build a model to forecast demand? How can you ensure that your model remains up-to-date and accurate as new data becomes available?

By practicing case study interview questions , you can sharpen problem-solving skills, and walk into future data science interviews more confidently.

Remember to practice consistently and stay up-to-date with relevant industry trends and techniques.

Final Thoughts

Data science case study interviews are more than just technical assessments; they’re opportunities to showcase your problem-solving skills and practical knowledge.

Furthermore, these interviews demand a blend of technical expertise, clear communication, and adaptability.

Remember, understanding the problem, exploring insights, and presenting coherent potential solutions are key.

By honing these skills, you can demonstrate your capability to solve real-world challenges using data-driven approaches. Good luck on your data science journey!

Frequently Asked Questions

How would you approach identifying and solving a specific business problem using data.

To identify and solve a business problem using data, you should start by clearly defining the problem and identifying the key metrics that will be used to evaluate success.

Next, gather relevant data from various sources and clean, preprocess, and transform it for analysis. Explore the data using descriptive statistics, visualizations, and exploratory data analysis.

Based on your understanding, build appropriate models or algorithms to address the problem, and then evaluate their performance using appropriate metrics. Iterate and refine your models as necessary, and finally, communicate your findings effectively to stakeholders.

Can you describe a time when you used data to make recommendations for optimization or improvement?

Recall a specific data-driven project you have worked on that led to optimization or improvement recommendations. Explain the problem you were trying to solve, the data you used for analysis, the methods and techniques you employed, and the conclusions you drew.

Share the results and how your recommendations were implemented, describing the impact it had on the targeted area of the business.

How would you deal with missing or inconsistent data during a case study?

When dealing with missing or inconsistent data, start by assessing the extent and nature of the problem. Consider applying imputation methods, such as mean, median, or mode imputation, or more advanced techniques like k-NN imputation or regression-based imputation, depending on the type of data and the pattern of missingness.

For inconsistent data, diagnose the issues by checking for typos, duplicates, or erroneous entries, and take appropriate corrective measures. Document your handling process so that stakeholders can understand your approach and the limitations it might impose on the analysis.

What techniques would you use to validate the results and accuracy of your analysis?

To validate the results and accuracy of your analysis, use techniques like cross-validation or bootstrapping, which can help gauge model performance on unseen data. Employ metrics relevant to your specific problem, such as accuracy, precision, recall, F1-score, or RMSE, to measure performance.

Additionally, validate your findings by conducting sensitivity analyses, sanity checks, and comparing results with existing benchmarks or domain knowledge.

How would you communicate your findings to both technical and non-technical stakeholders?

To effectively communicate your findings to technical stakeholders, focus on the methodology, algorithms, performance metrics, and potential improvements. For non-technical stakeholders, simplify complex concepts and explain the relevance of your findings, the impact on the business, and actionable insights in plain language.

Use visual aids, like charts and graphs, to illustrate your results and highlight key takeaways. Tailor your communication style to the audience, and be prepared to answer questions and address concerns that may arise.

How do you choose between different machine learning models to solve a particular problem?

When choosing between different machine learning models, first assess the nature of the problem and the data available to identify suitable candidate models. Evaluate models based on their performance, interpretability, complexity, and scalability, using relevant metrics and techniques such as cross-validation, AIC, BIC, or learning curves.

Consider the trade-offs between model accuracy, interpretability, and computation time, and choose a model that best aligns with the problem requirements, project constraints, and stakeholders’ expectations.

Keep in mind that it’s often beneficial to try several models and ensemble methods to see which one performs best for the specific problem at hand.

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Data Engineer Interview Questions And Answers (2024)

Join over 2 million students who advanced their careers with 365 Data Science. Learn from instructors who have worked at Meta, Spotify, Google, IKEA, Netflix, and Coca-Cola and master Python, SQL, Excel, machine learning, data analysis, AI fundamentals, and more.

Data engineer interview questions are a major component of your interview preparation process. However, if you want to maximize your chances of landing a data engineer job , you must also be aware of how the data engineer interview process is going to unfold.

This article is designed to help you navigate the data engineer interview landscape with confidence. Here’s what you will learn:

• the most important skills required for a data engineer position;
• a list of real data engineer questions and answers (practice makes perfect, right?);
• how the data engineer interview process goes down in 3 leading companies.

As a bonus, we’ll reveal 3 common mistakes you should avoid at all costs during your data engineer interview questions preparation.

But first things first…

What skills do you need to become a data engineer?

Skills and qualifications are the most crucial part of your preparation for a data engineer position. Here are the top 5 must-have skills for anyone aiming for a data engineer career:

• Knowledge of data modeling for both data warehousing and Big Data;
• Experience in ETLs;
• Experience in the Big Data space (Hadoop Stack like M/R, HDFS, Pig, Hive, etc.);
• SQL and Python ;
• Mathematics;
• Data visualization skills (e.g., Tableau or PowerBI).

If you need to improve your skillset to launch a successful career as a data engineer, you can register for the complete 365 Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses, and build up step-by-step experience with SQL, Python, R, Power BI and Tableau.

What are the most common data engineer interview questions you should be familiar with?

General data engineer interview questions.

Usually, interviewers start the conversation with a few more general questions. Their aim is to take the edge off and prepare you for the more complex data engineering questions ahead. Here are a few that will help you get off to a flying start.

1. How did you choose a career in data engineering?

How to answer.

The answer to this question helps the interviewer learn more about your education, background and work experience. You might have chosen the data engineering field as a natural continuation of your degree in Computer Science or Information Systems. Maybe you’ve had similar jobs before, or you’re transitioning from an entirely different career field. In any case, don’t shy away from sharing your story and highlighting the skills you’ve gained throughout your studies and professional path.

Answer Example

"Ever since I was a child, I have always had a keen interest in computers. When I reached senior year in high school, I already knew I wanted to pursue a degree in Information Systems. While in college, I took some math and statistics courses which helped me land my first job as a Data Analyst for a large healthcare company. However, as much as I liked applying my math and statistical knowledge, I wanted to develop more of my programming and data management skills. That’s when I started looking into data engineering. I talked to experts in the field and took online courses to learn more about it. I discovered it was the ideal career path for my combination of interests and skills. Luckily, within a couple of months, a data engineering position opened up in my company and I had the chance to transfer without a problem."

2. What do you think is the hardest aspect of being a data engineer?

Smart hiring managers know not all aspects of a job are easy. So, don’t hesitate to answer this question honestly. You might think its goal is to make you pinpoint a weakness. But, in fact, what the interviewer wants to know is how you managed to resolve something you struggled with.

“As a data engineer, I’ve mostly struggled with fulfilling the needs of all the departments within the company. Different departments often have conflicting demands. So, balancing them with the capabilities of the company’s infrastructure has been quite challenging. Nevertheless, this has been a valuable learning experience for me, as it’s given me the chance to learn how these departments work and their role in the overall structure of the company.”

3. Can you think of a time where you experienced an unexpected problem with bringing together data from different sources? How did you eventually solve it?

This question gives you the perfect opportunity to demonstrate your problem-solving skills and how you respond to sudden changes of the plan. The question could be data-engineer specific, or a more general one about handling challenges. Even if you don’t have particular experience, you can still give a satisfactory hypothetical answer.

“In my previous work experience, my team and I have always tried to be ready for any issues that may arise during the ETL process. Nevertheless, every once in a while, a problem will occur completely out of the blue. I remember when that happened while I was working for a franchise company. Its system required for data to be collected from various systems and locations. So, when one of the franchises changed their system without prior notification, this created quite a few loading issues for their store’s data. To deal with this issue, first I came up with a short-term solution to get the essential data into the company’s corporate wide-reporting system. Once I took care of that, I started developing a long-term solution to prevent such complications from happening again.”

4. Data engineers collaborate with data architects on a daily basis. What makes your job as a data engineer different?

How to answer.

With this question, the interviewer is most probably trying to see if you understand how job roles differ within a data warehouse team. However, there is no “right” or “wrong” answer to this question. The responsibilities of both data engineer and data architects vary (or overlap) depending on the requirements of the company/database maintenance department you work for.

“Based on my work experience, the differences between the two job roles vary from company to company. Yes, it’s true that data engineers and data architects work closely together. Still, their general responsibilities differ. Data architects are in charge of building the data architecture of the company’s data systems and managing the servers. They see the full picture when it comes to the dissemination of data throughout the company. In contrast, data engineers focus on testing and maintaining of the architecture, rather than on building it. Plus, they make sure that the data available to analysts within the organization is reliable and of the necessary high quality.”

5. Can you tell us a bit more about the data engineer certifications you have earned?

Certifications prove to your future employer that you’ve invested time and effort to get formal training for a skill, rather than just pick it up on the job. The number of certificates under your belt also shows how dedicated you are to expanding your knowledge and skillset. Recency is also important, as technology in this field is rapidly evolving, and upgrading your skills on a regular basis is vital. However, if you haven’t completed any courses or online certificate programs, you can mention the trainings provided by past employers or the current company you work for. This will indicate that you’re up-to-date with the latest advancements in the data engineering sphere.

“Over the past couple of years, I’ve become a certified Google Professional Data Engineer, and I’ve also earned a Cloudera Certified Professional credential as a Data Engineer. I’m always keeping up-to-date with new trainings in the field. I believe that’s the only way to constantly increase my knowledge and upgrade my skillset. Right now, I’m preparing for the IBM Big Data Engineer Certificate Exam. In the meantime, I try to attend big data conferences with recognized speakers, whenever I have the chance."

Technical Data Engineer Interview Questions

The technical data engineer questions help the interviewer assess 2 things: whether you have the skills necessary for the role; and if you’re experienced with (or willing to advance in) the systems and programs utilized in the company. So, here’s a list of technical questions you can practice with.

6. Which ETL tools have you worked with? Do you have a favorite one? If so, why?

The hiring manager needs to know that you’re no stranger to the ETL process and you have some experience with different ETL tools. So, once you enumerate the tools you’ve worked with and point out the one you favor, make sure to substantiate your preference in a way that demonstrates your expertise in the ETL process.

“I have experience with various ETL tools, such as IBM Infosphere, SAS Data Management, and SAP Data Services. However, if I have to pick one as my favorite, that would be Informatica’s PowerCenter. In my opinion, what makes it the best out there is its efficiency. PowerCenter has a very top performance rate and high flexibility which, I believe, are the most important properties of an ETL tool. They guarantee access to the data and smoothly running business data operations at all times, even if changes in the business or its structure take place."

7. Have you built data systems using the Hadoop framework? If so, please describe a particular project you’ve worked on.

Hadoop is a tool that many hiring managers ask about during interviews. You should know that whenever there’s a specific question like that, it’s highly likely that you’ll be required to use this particular tool on the job. So, to prepare, do your homework and make sure you’re familiar with the languages and tools the company uses. More often than not, you can find that information in the job description. If you’re experienced with the tool, give a detailed explanation of your project to highlight your skills and knowledge of the tool’s capabilities. In case you haven’t worked with this tool, the least you could do is do some research to demonstrate some basic familiarity with the tool’s attributes.

“I’ve used the Hadoop framework while working on a team project focused on increasing data processing efficiency. We chose to implement it because of its ability to increase data processing speeds while, at the same time, preserving quality through its distributed processing. We also decided to implement Hadoop because of its scalability, as the company I worked for expected a considerable increase in its data processing needs over the next few months. In addition, Hadoop is an open-source network which made it the best option, keeping in mind the limited resources for the project. Not to mention that it’s Java-based, so it was easy to use by everyone on the team and no additional training was required.”

8. Do you have experience with a cloud computing environment? What are the pros and cons of working in one?

Data engineers are well aware that there are pros and cons to cloud computing. That said, even if you lack prior experience working in cloud computing, you must be able to demonstrate a certain level of understanding of its advantages and shortcomings. This will show the hiring manager that you’re aware of the present technological issues in the industry. Plus, if the position you’re interviewing for requires using a cloud computing environment, the hiring manager will know that you’ve got a basic idea of the possible challenges you might face.

“I haven’t had the chance to work in a cloud computing environment yet. However, I have a good overall idea of its pros and cons. On the plus side, cloud computing is more cost-effective and reliable. Most providers sign agreements that guarantee a high level of service availability which should decrease downtimes to a minimum. On the negative side, the cloud computing environment may compromise data security and privacy, as the data is kept outside the company. Moreover, your control would be limited, as the infrastructure is managed by the service provider. All things considered, cloud computing could be both right or wrong choice for a company, depending on its IT department structure and the resources at hand.”

9. In your line of work, have you introduced new data analytics applications? If so, what challenges did you face while introducing and implementing them?

New data applications are high-priced, so introducing such within a company doesn’t happen that often. Nevertheless, when a company decides to invest in new data analytics tools, this could turn into quite an ambitious project. The new tools must be connected to the current systems in the company, and the employers who are going to use them should be formally trained. Additionally, maintenance of the tools should be administered and carried out on a regular basis. So, if you have prior experience, point out the obstacles you’ve overcome or list some scenarios of what could have gone wrong. In case you lack relevant experience, describe what you know about the process in detail. This will let the hiring manager know that, if a problem arises, you have the basic know-how that would help you through.

“As a data engineer, I’ve taken part in the introduction of a brand-new data analytics application in the last company I’ve worked for. The whole process requires a well-thought-out plan to ensure the smoothest transition possible. However, even the most careful planning can’t rule out unforeseen issues. One of them was the high demand for user licenses which went beyond our expectations. The company had to reallocate financial resources to obtain additional licenses. Furthermore, training schedules had to be set up in a way that doesn’t interrupt the workflow in different departments. In addition, we had to optimize our infrastructure, so that it could support the considerably higher number of users.”

10. What is your experience level with NoSQL databases? Tell me about a situation where building a NoSQL database was a better solution than building a relational database.

There are certain pros and cons of using one type of database compared to another. To give the best possible answer, try to showcase your knowledge about each and back it up with an example situation that demonstrates how you have applied (or would apply) your know-how to a real-world project.

“Building a NoSQL database can be beneficial in some situations. Here’s a situation from my experience that first comes to my mind. When the franchise system in the company I worked for was increasing in size exponentially, we had to be able to scale up quickly in order to make the most of all the sales and operational data we had on hand.

But here’s the thing. Scaling out is the better option, compared to scaling up with bigger servers, when it comes to handling increases data processing loads. Scaling out is also more cost-effective and it’s easier to accomplish through NoSQL databases. The latter can deal with larger volumes of data. And that can be crucial when you need to respond quickly to considerable shifts in data loads in the future. Yes, it’s true that relational databases have better connectivity to various analytics tools. However, as more of those are being developed, there’s definitely a lot more coming from NoSQL databases in the future. That said, the additional training some developers might need is certainly worth it.”

By the way, if you’re finding this answer useful, consider sharing this article, so others can benefit from it, too. Helping fellow aspiring data engineers reach their goals is one of the things that make the data science community special.

11. What’s your experience with data modeling? What data modeling tools have you used in your work experience?

As a data engineer, you probably have some experience with data modeling. In your answer, try not only to list the relevant tools you have worked with, but also mention their pros and cons. This question also gives you a chance to highlight your knowledge of data modeling in general.

“I’ve always done my best to be familiar with the data models in the companies I’ve worked for, regardless of my involvement with the data modeling process. This is one of the ways I gain a deeper understanding of the whole system. In my work experience, I’ve utilized Oracle SQL Developer Data Modeler to develop two types of models. Conceptual models for our work with stakeholders, and logical data models which make it possible to define data models, structures and relationships within the database.”

Behavioral Data Engineer Questions

Behavioral data engineer interview questions give the interviewer a chance to see how you have handled unforeseen data engineering issues or teamwork challenges in your experience. The answers you provide should reassure your future employer that you can deal with high-pressure situations and a variety of challenges. Here are a few examples to consider in your preparation.

12. Data maintenance is one of the routine responsibilities of a data engineer. Describe a time when you encountered an unexpected data maintenance problem that made you search for an out-of-the-box solution".

Usually, data maintenance is scheduled and covers a particular task list. Therefore, when everything is operating according to plan, the tasks don’t change as often. However, it’s inevitable that an unexpected issue arises every once in a while. As this might cause uncertainty on your end, the hiring manager would like to know how you would deal with such high-pressure situations.

“It’s true that data maintenance may come off as routine. But, in my opinion, it’s always a good idea to closely monitor the specified tasks. And that includes making sure the scripts are executed successfully. Once, while I was conducting an integrity check, I located a corrupt index that could have caused some serious problems in the future. This prompted me to come up with a new maintenance task that prevents corrupt indexes from being added to the company’s databases.”

13. Data engineers generally work “backstage”. Do you feel comfortable with that or do you prefer being in the “spotlight”?

The reason why data engineers mostly work “backstage” is that making data available comes much earlier in the data analysis project timeline. That said, c-level executives in the company are usually more interested in the later stages of the work process. More specifically, their goal is to understand the insights that data scientists extract from the data via statistical and machine learning models. So, your answer to this question will tell the hiring manager if you’re only able to work in the spotlight, or if you thrive in both situations.

“As a data engineer, I realize that I do most of my work away from the spotlight. But that has never been that important to me. I believe what matters is my expertise in the field and how it helps the company reach its goals. However, I’m pretty comfortable being in the spotlight whenever I need to be. For example, if there’s a problem in my department which needs to be addressed by the company executives, I won’t hesitate to bring their attention to it. I think that’s how I can further improve my team’s work and reach better results for the company.”

14. Do you have experience as a trainer in software, applications, processes or architecture? If so, what do you consider as the most challenging part?

As a data engineer, you may often be required to train your co-workers on the new processes or systems you’ve created. Or you may have to train new teammates on the already existing architectures and pipelines. As technology is constantly evolving, you might even have to perform recurring trainings to keep everyone on track. That said, when you talk about a challenge you’ve faced, make sure you let the interviewer know how you handled it.

“Yes, I have experience training both small and large groups of co-workers. I think the most challenging part is to train new employees who already have significant experience in another company. Usually, they’re used to approaching data from an entirely different perspective. And that’s a problem because they struggle to accept the way we handle projects in our company. They’re often very opinionated and it takes time for them to realize there’s more than one solution to a certain problem. However, what usually helps is emphasizing how successful our processes and architecture have proven to be so far. That encourages them to open their minds to the alternative possibilities out there.”

15. Have you ever proposed changes to improve data reliability and quality? Were they eventually implemented? If not, why not?

One of the things hiring managers value most is constant improvements of the existing environment, especially if you initiate those improvements yourself, as opposed to being assigned to do it. So, if you’re a self-starter, definitely point this out. This will showcase your ability to think creatively and the importance you place on the overall company’s success. If you lack such experience, explain what changes you would propose as a data engineer. In case your ideas were not implemented for reasons such as lack of financial resources, you can mention that. However, try to focus on your continuous efforts to find novel ways to improve data quality.

“Data quality and reliability have always been a top priority in my work. While working on a specific project, I discovered some discrepancies and outliers in the data stored in the company’s database. Once I’ve identified several of those, I proposed to develop and implement a data quality process in my department’s routine. This included bi-weekly meetups with coworkers from different departments where we would identify and troubleshoot data issues. At first, everyone was worried that this would take too much time off their current projects. However, in time, it turned out it was worth it. The new process prevented the occurrence of larger (and more costly) issues in the future."

16. Have you ever played an active role in solving a business problem through the innovative use of existing data?

Hiring managers are looking for self-motivated people who are eager to contribute to the success of a project. Try to give an example where you came up with a project idea or you took charge of a project. It’s best if you point out what novel solution you proposed, instead of focusing on a detailed description of the problem you had to deal with.

“In the last company I worked for, I took active part in a project that aimed to identify the reason’s for the high employee turnover rate. I started by closely observing data from other areas of the company, such as Marketing, Finance, and Operations. This helped me find some high correlations of data in these key areas with employee turnover rates. Then, I collaborated with the analysts in those departments to gain a better understanding of the correlations in question. Ultimately, our efforts resulted in strategic changes that had a positive influence over the employee turnover rates.”

17. Which non-technical skills do you find most valuable in your role as a data engineer?

Although technical skills are of major importance if you want to advance your data engineer career, there are many non-engineering skills that could aid your success. In your answer, try to avoid the most obvious examples, such as communication or interpersonal skills.

“I’d say the most useful skills I’ve developed over the years are multitasking and prioritizing. As a data engineer, I have to prioritize or balance between various tasks daily. I work with many departments in the company, so I receive tons of different requests from my coworkers. To cope with those efficiently, I need to put fulfilling the most urgent company needs first without neglecting all the other requests. And strengthening the skills I mentioned has really helped me out.”

Brainteasers

Interviewers use brainteasers to test both your logical and creative thinking. These questions also help them assess how quickly you can resolve a task that requires an out-of-the-box approach.

18. You have eight balls of the same size. Seven of them weigh the same, and one of them weighs slightly more. How can you find the ball that is heavier by using a balance and only two attempts at weighing?

You can put six of the balls on the balance. If one of the sides is heavier you will know that the heavier ball is on that side. If not, the heavier ball is among the two that you did not measure and it will be really easy to determine precisely which ball is heavier with your second weighing.

After you determine which side is heavier, you will have 3 balls left to choose from. You have another attempt at weighing left. You can put two of the balls on the balance and see if one of them is heavier. If it is, then you have found the heavier ball. If it is not, then the third ball is the one that is heavier.

19. A windowless room has three light bulbs. You are outside the room with 3 switches, each of them controlling one of the light bulbs. If you were told that you can enter the room only once, how are you going to tell which switch controls which light bulb?

You have to be creative in order to solve this one. You switch on two of the light bulbs and then wait for 30 minutes. Then you switch off one of them and enter the room. You will know which switch controls the light bulb that is on. Here is the tough part. How are you going to be able to determine which switch corresponds to the other two light bulbs? You will have to touch them. Yes. That’s right. Touch them and feel which one is warm. That will be the other bulb that you had turned on for 30 minutes.

You will be in serious trouble if the interviewer says that the light bulbs are LED (given that they don’t emit heat).

Guesstimate

Although guesstimates aren’t an obligatory part of the data engineer interview process, many interviewers would ask such a question to assess your quantitative reasoning and approach to solving complex problems. Here’s a good example.

20. How many gallons of white house paint are sold in the US every year?

Find the number of homes in the US: Assuming that there are 300 million people in the US and the average household contains 2.5 people then we can conclude that there are 120 million homes in the US.

Number of houses: Many people live in apartments and other types of buildings different than houses. Let’s assume that the percentage of people living in houses is 50%. Hence, there are 60 million houses.

Houses that are painted in white: Although white is the most popular color, many people choose different paint colors for their houses or do not need to paint them (using other types of techniques in order to cover the external surface of the house). Let’s hypothesize that 30% of all houses are painted in white, which makes 18 million houses that are painted in white.

Repainting: People need to repaint their houses after a given amount of years. For the purposes of this exercise, let’s hypothesize that people repaint their houses once every 9 years, which means that every year 2 million houses are repainted in white.

I have never painted a house, but let’s assume that in order to repaint a house you need 30 gallons of white paint. This means the total US market for white house paint is 60 million gallons.

What is the data engineer interview process like?

A phone screen with a recruiter or a team member? How many onsite interviews you should be ready for? Will there be one or multiple interviewers?

Short answer: It depends on the company, its hiring policy and interviewing approach.

That said, here is what you can expect from a data engineer job interview at three top companies – Yahoo, Facebook, and Walmart. We believe these overviews will give you a good initial idea of what happens behind the scenes.

Generally, Yahoo recruit candidates from the top 10-20 schools. However, you can still get a data engineer interview through large job search platforms, such as Indeed.com and Glassdoor. Or, if you are lucky enough – with an internal referral. Anyhow, once you make the cut, you can expect a phone screen with a manager or a team lead. What about the onsite interviews? Usually, you’ll interview with 6-7 data engineer team members for about 45 minutes each. Each interview will focus on a different area, but all of them have a similar structure. A short general talk (5 minutes), followed by a coding question (20 minutes) and a data engineering question (20 minutes). The latter will often tap into your previous experience to solve a current data engineering issue the company is experiencing.

In the end, you’ll have a more general talk with a senior employee. At the same time, the interviewers will gather to share their feedback on your performance and check in with the hiring manager. If you’ve passed the data engineer interview with flying colors, you could get a decision on the day of the interview! However, if a few days have passed and you haven’t received an answer, don’t be shy to send HR a polite update request.

Usually, the data engineering interviewing process starts with an email or a phone call with a recruiter, followed by a phone screen or an in-person interview. The screening interview is conducted by a coworker and takes about 1 hour. It consists of SQL questions and online test coding tasks that you have to solve through a collaborative editor (CoderPad) in a programming language of your choice. Also, prepare to answer questions related to your resume, skills, interests, and motivation. If those go well, they'll invite you to a longer series of interviews at the Facebook office - 5 hours of in-person interviews, including a 1-hour lunch interview.

Three of the onsite interviews are focused on problem-solving. You’ll be questioned about data engineering issues that the company is facing and how you can help them solve them, for example, how to identify the metrics for performance for this specific feature) and you will be expected to write SQL and actual code for the context of the problem itself. There is also a behavioral interview portion, asking you about your work experience, and how you deal with interpersonal problems. Finally, there is an informal lunch conversation where you can ask about the work culture and other day-to-day questions.

What’s typical of Facebook interviews is that many data engineer interview questions focus on a deep understanding of their product, so make sure you demonstrate both knowledge and genuine interest in the data engineer job.

Once the interviews are over, everyone you’ve interviewed with compare notes to decide if you’ll be successful in the data engineer role. Then all left to do is wait for your recruiter to contact you with feedback from the interview. Or, if you haven’t heard from a company rep within a week or so, take matters into your own hands and send a kind follow-up email.

The data engineer interview process will usually start with a phone screen, followed by 4 technical interviews (expect some coding, big data, data modeling, and mathematics) and 1 lunch interview. More often than not, there is one more data engineer technical interview with a hiring manager (and guess what - it involves some more coding!). Anything specific to remember? Yes. Walmart has been utilizing huge amounts of big data, even before it was coined as “big”. MapReduce, Hive, HDFS, and Spark are all used internally by their data science and data engineering teams. That said, a little bit of practice every day goes a long way. And, if you diligently prepare for some coding and big data questions, you have every chance of becoming a data engineer in the world’s biggest retail corporation.

What common mistakes to avoid in your data engineer interview questions preparation?

We know that sometimes the devil’s in the details. And we wouldn’t want you to miss a single detail that could cost you your success! So, here are 3 common mistakes you should definitely refrain from making:

Not practicing behavioral data engineer interview questions

Even if you have the technical part covered, that doesn’t necessarily mean smooth sailing! Behavioral questions are becoming increasingly important, as they tell the interviewer more about your personality, how you handle conflicts and problematic work situations. So, remember to prepare for those by rehearsing some relevant stories from your past experience and getting familiar with the behavioral data engineer interview questions we’ve listed.

Skipping the mock interview

Are you so deep into your interview preparation process that you’ve cut all ties with the outside world? Big mistake! Snap out of it now, call a fellow data engineer and ask them to do a mock interview with you. Every interview has a performance side to it, and just imagining how you’re going to act or sound wouldn’t give you a realistic idea. So, while you’re doing the mock interview, pay special attention to your body language and mannerisms, as well as to your tone of voice and pace of speech. You’ll be amazed by the insight you’re going to get!

Getting discouraged

There’s one more thing you should remember about interviews. Once you pass the easier problems, you’re bound to get to the harder data engineer interview questions. But no matter how difficult they seem, don’t give up. Stay cool, calm, and collected, and don’t hesitate to ask for guidance or additional explanations. If anything, this will prove two things: that you’re not afraid of challenging situations; and you’re willing to collaborate to find an efficient solution.

In Conclusion

Now that you’re well-familiar with the data engineer interview questions and the most important things to remember about the interview process itself, you should be much more confident in your interview preparation for that position. If you’re eager to explore more data engineer interview questions, follow the link to our all-comprising article Data Science Interview Questions . However, if you feel that you lack some of the essential skills required for the job, check out the  complete Data Science Program . In case you aren’t sure if you want to turn your interest in data science into a full-fledged career, we also offer a  free preview version of the Data Science Program . You’ll receive 12 hours of beginner to advanced content for free. It’s a great way to see if the program is right for you.

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15 Must-Ask Data Engineer Interview Questions

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Data engineering is a rapidly growing profession that involves designing, building, and managing data pipelines, databases, and infrastructure to support data-driven decision-making.

With the exponential growth of data in recent years, businesses across industries rely on data engineers to transform raw data into usable insights and drive innovation.

Key facts and data

• Median salary: The median annual salary for data engineers is around $98,230 according to BLS.
• Industry growth: The employment of data engineers is projected to grow 35% from 2022 to 2032 , much faster than average.
• Job outlook: It’s projected that there will be around 17,700 new data engineering positions every year.
• Typical entry-level education: To become a data engineer, you usually need a bachelor’s degree in mathematics, statistics, computer science, or a related science. Some employers require a master’s or doctoral degree.

While recruiting for your company, during data engineering interviews, you will aim to assess candidates' technical skills, problem-solving abilities, and their ability to design scalable and efficient data solutions, while also evaluating their hard skills related to coding.

Prepare for your upcoming interview with this list of 15 commonly asked data engineering interview questions, along with sample answers to look for.

1. How would you design a scalable data ingestion pipeline for real-time streaming data?

Aim: Assessing the candidate's ability to design data pipelines and handle streaming data.

Key skills assessed: Data pipeline design, real-time data processing, scalability.

What to look for

Look for candidates who mention components like Apache Kafka or Apache Flink for handling real-time data, and discuss strategies to ensure scalability, fault tolerance, and resilience.

Example answer

"To design a scalable data ingestion pipeline for real-time streaming data, I would incorporate Apache Kafka as the messaging system, along with Apache Flink for real-time data processing. I would ensure fault tolerance by implementing data replication and micro-batch processing to handle spikes in data volume."

2. How would you optimize a SQL query with performance issues?

Aim: Evaluating the candidate's SQL skills and ability to identify and fix performance bottlenecks.

Key skills assessed: SQL optimization, query optimization, performance tuning.

Look for candidates who mention techniques such as indexing, query rewriting, and using EXPLAIN to analyze query execution plans. They should also highlight the importance of understanding database schemas and optimizing joins.

"To optimize a SQL query with performance issues, I would start by analyzing the query execution plan using EXPLAIN. I would then consider indexing the relevant columns, rewriting the query to reduce unnecessary joins or subqueries, and ensuring the proper indexing of foreign key relationships."

3. How would you tackle data quality issues in a data pipeline?

Aim: Assessing the candidate's understanding of data quality principles and their problem-solving abilities.

Key skills assessed: Data quality management, data validation, error handling.

Look for candidates who emphasize the importance of data validation, error handling mechanisms, and automated data quality checks. They should also mention techniques such as outlier detection and duplicate removal.

"To tackle data quality issues in a data pipeline, I would implement automated data quality checks at various stages of the pipeline. This would involve validating data against predefined rules, handling error cases, and implementing outlier detection techniques. I would also ensure proper data cleansing techniques, such as removing duplicates."

4. How would you handle a large-scale data migration from one database to another?

Aim: Evaluating the candidate's experience with data migration and their ability to handle complex data scenarios.

Key skills assessed: Data migration, ETL (Extract, Transform, Load), data mapping.

When asking data engineer technical interview questions, focus on candidates who mention their experience with ETL tools like Apache Airflow or Informatica, and highlight the importance of data mapping and transforming data between different schemas. They should also discuss strategies for handling large volumes of data efficiently.

"For a large-scale data migration, I would leverage an ETL tool like Apache Airflow to automate the extraction, transformation, and loading process. I would carefully map the source and target schemas, handling any necessary data transformation along the way. To ensure efficiency, I would consider partitioning the data and using parallel processing techniques."

5. How would you approach designing a data warehouse architecture?

Aim: Assessing the candidate's understanding of data warehousing concepts and their ability to design scalable and robust architectures.

Key skills assessed: Data warehousing, architecture design, scalability.

Look for candidates who mention concepts like star and snowflake schema, dimensional modeling, and technologies like Amazon Redshift or Snowflake. They should also discuss strategies for data integration and ensuring optimal query performance.

"When designing a data warehouse architecture, I would adopt a star or snowflake schema based on the organization's requirements. I would use dimensional modeling techniques to structure the data for efficient querying. Technologies like Amazon Redshift or Snowflake can provide scalability and elasticity. I would also consider data integration strategies, such as incremental loading and ETL processes to maintain data consistency."

6. How do you ensure data security and privacy in a data engineering project?

Aim: Evaluating the candidate's understanding of data security practices and their ability to implement measures to protect sensitive data.

Key skills assessed: Data security, data privacy, encryption.

Look for candidates who mention techniques such as encryption, access controls, and anonymization. They should also discuss compliance with relevant data protection regulations like GDPR or HIPAA.

"To ensure data security and privacy , I would implement encryption mechanisms to protect sensitive data both at rest and in transit. I would set up access controls to limit access to authorized users and apply anonymization techniques when necessary. Compliance with data protection regulations like GDPR or HIPAA would also be a top priority."

💡 See also: Data Protection and Privacy Across Borders: Enterprise Global HR Compliance

7. How do you handle data versioning and lineage in a data engineering project?

Aim: Assessing the candidate's ability to track data changes and maintain data lineage in complex data pipelines.

Key skills assessed: Data versioning, data lineage, data governance.

Look for candidates who mention version control systems like Git or Apache Atlas for tracking changes. They should also discuss techniques like metadata management and data cataloging to ensure data lineage traceability.

"To handle data versioning and lineage, I would utilize a version control system like Git to track changes in the data pipeline code. I would also implement metadata management tools like Apache Atlas, which can capture data lineage information. Proper data cataloging practices would ensure the traceability of data transformations and changes."

8. How would you approach troubleshooting and debugging a complex data engineering pipeline?

Aim: Evaluating the candidate's problem-solving abilities and their approach to identifying and resolving issues in data pipelines.

Key skills assessed: Troubleshooting, debugging, problem-solving.

Look for candidates who mention techniques like logging, monitoring, and error handling mechanisms. They should also discuss their experience with tools like Apache Spark or AWS CloudWatch for diagnosing and resolving issues.

"When troubleshooting a complex data engineering pipeline, I would rely on logging and monitoring systems to identify potential issues. I would analyze error logs, exception handling mechanisms, and leverage tools like Apache Spark or AWS CloudWatch to gain insights into the pipeline's behavior. I would then apply systematic problem-solving techniques to identify and resolve the root cause of the issue."

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9. How do you ensure data consistency when processing data in a distributed system?

Aim: Assessing the candidate's understanding of distributed systems and their ability to handle data consistency in a distributed environment.

Key skills assessed: Distributed systems, data consistency, fault tolerance.

Look for candidates who mention techniques like distributed transactions or the use of consensus algorithms like Raft or Paxos. They should also discuss strategies for handling partial failures and maintaining data integrity.

"To ensure data consistency in a distributed system, I would adopt techniques like distributed transactions that maintain atomicity, consistency, isolation, and durability (ACID) properties. Consensus algorithms like Raft or Paxos can handle distributed agreement and guarantee data consistency. I would also consider fault-tolerant mechanisms to handle partial failures and ensure data integrity."

📖 Learn more: What is a DPA (Data Processing Agreement)?

10. How would you approach data modeling for a NoSQL database?

Aim: Evaluating the candidate's familiarity with NoSQL databases and their ability to design efficient data models.

Key skills assessed: NoSQL databases, data modeling, scalability.

Look for candidates who mention NoSQL databases like MongoDB or Cassandra and discuss techniques like denormalization and document-oriented modeling. They should also highlight the importance of understanding query patterns and ensuring data scalability.

"When approaching data modeling for a NoSQL database, I would consider the specific requirements of the application and the expected query patterns. I would denormalize the data to optimize query performance and ensure data scalability. Document-oriented modeling in databases like MongoDB would allow us to store data in a more flexible and schema-less manner."

11. How do you ensure data lineage and auditability in an event-driven architecture?

Aim: Assessing the candidate's understanding of event-driven architectures and their ability to track data changes and ensure data integrity.

Key skills assessed: Event-driven architecture, data lineage, data integrity.

Look for candidates who mention technologies like Apache Kafka or Apache Pulsar for event streaming and discuss techniques like event sourcing or change data capture. They should also emphasize the importance of logging and auditing mechanisms.

"To ensure data lineage and auditability in an event-driven architecture, I would leverage technologies like Apache Kafka or Apache Pulsar for event streaming. I would implement techniques like event sourcing or change data capture to capture and store every data change. Logging and auditing mechanisms would provide visibility into events and ensure data integrity."

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12. How do you handle data schema evolution in a data engineering project?

Aim: Evaluating the candidate's ability to handle evolving data schemas and adapt data pipelines accordingly.

Key skills assessed: Data schema evolution, data pipeline maintenance, adaptability.

Look for candidates who mention techniques like schema evolution using Avro or Protobuf and discuss the importance of maintaining backward compatibility. They should also emphasize the need for rigorous testing and versioning of data structures.

"When handling data schema evolution, I would adopt techniques like using Avro or Protobuf to define schema changes in a backward-compatible manner. This ensures that existing data pipelines can continue to process new data without any disruptions. Rigorous testing and versioning of data structures would be necessary to guarantee smooth transitions and prevent data inconsistency."

13. How do you approach data governance in a data engineering project?

Aim: Assessing the candidate's understanding of data governance principles and their ability to implement data management best practices.

Key skills assessed: Data governance, data management, data quality.

Look for candidates who discuss the importance of data governance frameworks, data lineage, and data cataloging. They should also mention techniques like data profiling and metadata management to ensure data quality and compliance.

"To approach data governance in a data engineering project, I would implement a data governance framework that defines policies, roles, and responsibilities. Data lineage and data cataloging practices would provide transparency and traceability. Techniques like data profiling and metadata management can ensure data quality and compliance with regulatory standards."

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14. How do you stay updated with the latest data engineering trends and technologies?

Aim: Evaluating the candidate's passion for learning and their commitment to professional growth.

Key skills assessed: Continuous learning , technological awareness, adaptability.

Among other questions to ask an engineer, it’s also critical to identify candidates who invest in their own continuous growth. Look for those who mention resources like online forums, blogs, or industry conferences where they stay updated. They should also discuss personal projects or collaborations that demonstrate their initiative to learn and apply new technologies.

"To stay updated with the latest data engineering trends and technologies, I actively participate in online forums like Stack Overflow and follow influential blogs in the field. I also attend industry conferences and webinars to learn from experts and network with peers. I enjoy working on personal data engineering projects and collaborating with colleagues to explore and apply new technologies."

15. Describe a challenging data engineering project you worked on and how you overcame the challenges.

Aim: Assessing the candidate's problem-solving abilities and their ability to reflect on past experiences.

Key skills assessed: Problem-solving, project management, adaptability.

Look for candidates who provide a detailed description of the project, highlight the challenges they faced, and discuss the strategies they adopted to overcome those challenges. They should also emphasize the lessons learned and the skills they gained from the experience.

"One of the most challenging data engineering projects I worked on was implementing a real-time recommendation system for an e-commerce platform. The main challenge was handling the high data volume generated by user interactions and processing it in real-time. To overcome this, we designed a scalable data ingestion pipeline using Apache Kafka and implemented a microservices architecture for real-time data processing. We also incorporated machine learning models for personalized recommendations. It required extensive coordination and collaboration with cross-functional teams, and we overcame the challenges through agile project management practices and constant communication. This experience enhanced my skills in data processing, performance optimization, and project management."

Data engineering is a dynamic and crucial profession in today's data-driven world. By familiarizing themselves with these common engineering interview questions, recruiters and hiring managers can conduct successful interviews and ensure they’ve chosen the best candidate for the organization.

If you are a data engineer, interview questions and answers found in this article will help you show up for the interview well prepared. Remember to tailor your responses to your experiences and highlight relevant technical skills, problem-solving abilities, and adaptability.

Additional resources

• Job Description Templates : Use these customizable templates for your open roles and attract the right candidates worldwide.
• Recruitment Email Template Package : Save time and improve your recruitment email quality today.
• Hiring Top Tech Talent Guide : Learn how to streamline your developer recruitment process with tested practices and expert tips from OfferZen and Deel.

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Facebook data engineer interview case study and interview questions

Here is a Facebook data engineer interview case study.

Candidate : Jake

How it gets started : applied in May, a friend did an internal referral

Job level:  E5

Year of Experience : 5 - 10

Degree : M.S & B.S. in CS

Offer:  Yes

TC : ~450K USD

Location : Menlo Park, CA

Interview process : 2 months

Preparation:  2 months

Has a job:  yes

Decide to join:  Yes

• Technical screen round 1:

Python and SQL

• Technical screen round 2:

Final round (met 4 people)

Data Modeling

Leadership/Behavioral questions

Sample Python questions  (4 questions to be completed in one hour)

1. Fill the  None  values with the previous none None value

[1, None, 1, 2, None] --> [1, 1, 1, 2, 2]

Tips : You have to pay attention if there are consecutive Nones.

2. Write a function to return a list with words that don't have a match (case sensitive) between two strings

("Facebook is an awesome place", "Facebook Is an AWESOME place") --> ["is", "Is", "awesome", "AWESOME"]

3. Write a function that counts the frequency of a character 

('missisipi', 's') --> 3

4. Write a function that returns the key of the nth largest value in a dictionary 

Example: {'a': 1, 'b': 2, 'c': 100, 'd': 30}

n : 2 (2nd largest value)

output: 'd'

Sample SQL questions

• Percentage of paid customers who bought both product A and product B
• Percentage of sales attributed by promotion on the first day and last day of promotions.
• For each product A, find the top 5 other products that people also bought.

Sample data modeling interview questions

Design the backend datawarehouse of a ride-sharing app.

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Learn the skills needed to succeed as a data engineer, top 80+ data engineer interview questions and answers.

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Introduction to Data Science

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Whether you’re new to the world of big data and looking to break into a Data Engineering role or an experienced Data Engineer looking for a new opportunity, preparing for an upcoming interview can be overwhelming. Given how competitive this market is right now, it is important to be prepared for your interview. The following are some of the top data engineer interview questions and answers you can likely expect at your interview, along with reasons why these questions are asked and the type of answers that interviewers are typically looking for.

1. What is Data Engineering?

This may seem like a pretty basic data engineer interview questions, but regardless of your skill level, this may come up during your interview. Your interviewer wants to see what your specific definition of data engineering is, which also makes it clear that you know what the work entails.  So, what is it? In a nutshell, it is the act of transforming, cleansing, profiling, and aggregating large data sets. You can also take it a step further and discuss the daily duties of a data engineer, such as ad-hoc data query building and extracting, owning an organization’s data stewardship, and so on.

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2. Why did you choose a career in Data Engineering?

An interviewer might ask this question to learn more about your motivation and interest behind choosing data engineering as a career. They want to employ individuals who are passionate about the field. You can start by sharing your story and insights you have gained to highlight what excites you most about being a data engineer. 

3. How does a data warehouse differ from an operational database?

This data engineer interview question may be more geared toward those on the intermediate level, but in some positions, it may also be considered an entry-level question. You’ll want to answer by stating that databases using Delete SQL statements , Insert, and Update is standard operational databases that focus on speed and efficiency. As a result, analyzing data can be a little more complicated. With a data warehouse, on the other hand, aggregations, calculations, and select statements are the primary focus. These make data warehouses an ideal choice for data analysis.

4. What Do *args and **kwargs Mean?

If you’re interviewing for a more advanced role, you should be prepared to answer complex coding questions. This specific coding question is commonly asked in data engineering interviews, and you’ll want to answer by telling your interviewer that *args defines an ordered function and that **kwargs represent unordered arguments used in a function. To impress your interviewer, you may want to write down this code in a visual example to demonstrate your expertise.

5. As a data engineer, how have you handled a job-related crisis?

Data engineers have a lot of responsibilities, and it’s a genuine possibility that you’ll face challenges while on the job, or even emergencies. Just be honest and let them know what you did to solve the problem. If you have yet to encounter an urgent issue while on the job or this is your first data engineering role, tell your interviewer what you would do in a hypothetical situation. For example, you can say that if data were to get lost or corrupted, you would work with IT to make sure data backups were ready to be loaded, and that other team members have access to what they need.

6. Do you have any experience with data modeling?

Unless you are interviewing for an entry-level role, you will likely be asked this question at some point during your interview. Start with a simple yes or no. Even if you don’t have experience with data modeling, you’ll want to be at least able to define it: the act of transforming and processing fetched data and then sending it to the right individual(s). If you are experienced, you can go into detail about what you’ve done specifically. Perhaps you used tools like Talend, Pentaho, or Informatica. If so, say it. If not, simply being aware of the relevant industry tools and what they do would be helpful.

7. Why are you interested in this job, and why should we hire you? 

It is a fundamental data engineer interview question, but your answer can set you apart from the rest. To demonstrate your interest in the job, identify a few exciting features of the job, which makes it an excellent fit for you and then mention why you love the company. 

For the second part of the question, link your skills, education, personality, and professional experience to the job and company culture. You can back your answers with examples from previous experience. As you justify your compatibility with the job and company, be sure to depict yourself as energetic, confident, motivated, and culturally fit for the company. 

8. What are the essential skills required to be a data engineer?

Every company can have its own definition of a data engineer, and they match your skills and qualifications with the company's assessment. 

Here is a list of must-have skills and requirements if you are aiming to be a successful data engineer:

• Comprehensive knowledge about Data Modelling.
• Understanding about database design & database architecture. In-Depth Database Knowledge – SQL and NoSQL .
• Working experience of data stores and distributed systems like Hadoop (HDFS) .
• Data Visualization Skills.
• Experience in Data Warehousing and ETL (Extract Transform Load) Tools.
• You should have robust computing and math skills.
• Outstanding communication, leadership, critical thinking, and problem-solving capabilities are an added advantage. 

You can mention specific examples in which a data engineer would apply these skills.

9. Can you name the essential frameworks and applications for data engineers?

This data engineer interview question is often asked to evaluate whether you understand the critical requirements for the position and have the desired technical skills . In your answer, accurately mention the names of frameworks along with your level of experience  with each. 

You can list all of the technical applications like SQL , Hadoop, Python , and more, along with your proficiency level in each. You can also state the frameworks which want to learn more about if given the opportunity.

10. Are you experienced in Python, Java, Bash, or other scripting languages?

This question is asked to emphasize the importance of understanding scripting languages as a data engineer. It is essential to have a comprehensive knowledge of scripting languages, as it allows you to perform analytical tasks efficiently and automate data flow.

11. Can you differentiate between a Data Engineer and Data Scientist?

With this question, the recruiter is trying to assess your understanding of different job roles within a data warehouse team. The skills and responsibilities of both positions often overlap, but they are distinct from each other. 

Data Engineers develop, test, and maintain the complete architecture for data generation, whereas data scientists analyze and interpret complex data. They tend to focus on organization and translation of Big Data . Data scientists require data engineers to create the infrastructure for them to work.

12. What, according to you, are the daily responsibilities of a data engineer?

This question assesses your understanding of the role of a data engineer role and job description. 

You can explain some crucial tasks a data engineer like:

• Development, testing, and maintenance of architectures.
• Aligning the design with business requisites.
• Data acquisition and development of data set processes.
• Deploying machine learning and statistical models
• Developing pipelines for various ETL operations and data transformation
• Simplifying data cleansing and improving the de-duplication and building of data.
• Identifying ways to improve data reliability, flexibility, accuracy, and quality.

This is one of the most commonly asked data engineer interview questions.

13. What is your approach to developing a new analytical product as a data engineer?

The hiring managers want to know your role as a data engineer in developing a new product and evaluate your understanding of the product development cycle. As a data engineer, you control the outcome of the final product as you are responsible for building algorithms or metrics with the correct data.  

Your first step would be to understand the outline of the entire product to comprehend the complete requirements and scope. Your second step would be looking into the details and reasons for each metric. Think about as many issues that could occur, and it helps you to create a more robust system with a suitable level of granularity.

14. What was the algorithm you used on a recent project?

The interviewer might ask you to select an algorithm you have used in the past project and can ask some follow-up questions like:

• Why did you choose this algorithm, and can you contrast this with other similar ones? 
• What is the scalability of this algorithm with more data? 
• Are you happy with the results? If you were given more time, what could you improve?

These questions are a reflection of your thought process and technical knowledge. First, identify the project you might want to discuss. If you have an actual example within your area of expertise and an algorithm related to the company's work, then use it to pique the interest of your hiring manager. Secondly, make a list of all the models you worked with and your analysis. Start with simple models and do not overcomplicate things. The hiring managers want you to explain the results and their impact.

15. What tools did you use in a recent project?

Interviewers want to assess your decision-making skills and knowledge about different tools. Therefore, use this question to explain your rationale for choosing specific tools over others. 

• Walk the hiring managers through your thought process, explaining your reasons for considering the particular tool, its benefits, and the drawbacks of other technologies. 
• If you find that the company works on the techniques you have previously worked on, then weave your experience with the similarities.

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16. What challenges came up during your recent project, and how did you overcome these challenges?

Any employer wants to evaluate how you react during difficulties and what you do to address and successfully handle the challenges. 

When you talk about the problems you encountered, frame your answer using the STAR method:

• Situation: Brief them about the circumstances due to which problem occurred.
• Task: It is essential to elaborate on your role in overcoming the problem. For example, if you took a leadership role and provided a working solution, then showcasing it could be decisive if you were interviewing for a leadership position.
• Action: Walk the interviewer through the steps you took to fix the problem. 
• Result: Always explain the consequences of your actions. Talk about the learnings and insights gained by you and other stakeholders.

17. Have you ever transformed unstructured data into structured data?

It is an important question as your answer can demonstrate your understating of both the data types and your practical working experience. You can answer this question by briefly distinguishing between both categories. The unstructured data must be transformed into structured data for proper data analysis, and you can discuss the methods for transformation. You must share a real-world situation wherein you changed the unstructured data into structured data. If you are a fresh graduate and don't have professional experience, discuss information related to your academic projects.

18. What is Data Modelling? Do you understand different Data Models?

Data Modelling is the initial step towards data analysis and database design phase. Interviewers want to understand your knowledge. You can explain that is the diagrammatic representation to show the relation between entities. First, the conceptual model is created, followed by the logical model and, finally, the physical model. The level of complexity also increases in this pattern. 

19. Can you list and explain the design schemas in Data Modelling?

Design schemas are the fundamentals of data engineering, and interviewers ask this question to test your data engineering knowledge. In your answer, try to be concise and accurate. Describe the two schemas, which are Star schema and Snowflake schema. 

Explain that Star Schema is divided into a fact table referenced by multiple dimension tables, which are all linked to a fact table. In contrast, in Snowflake Schema, the fact table remains the same, and dimension tables are normalized into many layers looking like a snowflake.

20. How would you validate a data migration from one database to another?

The validity of data and ensuring that no data is dropped should be of utmost priority for a data engineer. Hiring managers ask this question to understand your thought process on how validation of data would happen. 

You should be able to speak about appropriate validation types in different scenarios. For instance, you could suggest that validation could be a simple comparison, or it can happen after the complete data migration. 

21. Have you worked with ETL? If yes, please state, which one do you prefer the most and why?

With this question, the recruiter needs to know your understanding and experience regarding the ETL (Extract Transform Load) tools and process. You should list all the tools in which you have expertise and pick one as your favourite. Point out the vital properties which make that tool stand out and validate your preference to demonstrate your knowledge in the ETL process.

22. What is Hadoop? How is it related to Big data? Can you describe its different components?

This question is most commonly asked by hiring managers to verify your knowledge and experience in data engineering. You should tell them that Big data and Hadoop are related to each other as Hadoop is the most common tool for processing Big data, and you should be familiar with the framework. 

With the escalation of big data, Hadoop has also become popular. It is an open-source software framework that utilizes various components to process big data. The developer of Hadoop is the Apache foundation, and its utilities increase the efficiency of many data applications. 

Hadoop comprises of mainly four components: 

• HDFS stands for Hadoop Distributed File System and stores all of the data of Hadoop. Being a distributed file system, it has a high bandwidth and preserves the quality of data.
• MapReduce processes large volumes of data.
• Hadoop Common is a group of libraries and functions you can utilize in Hadoop.
• YARN (Yet Another Resource Negotiator)deals with the allocation and management of resources in Hadoop. 

23. Do you have any experience in building data systems using the Hadoop framework? 

If you have experience with Hadoop, state your answer with a detailed explanation of the work you did to focus on your skills and tool's expertise. You can explain all the essential features of Hadoop. For example, you can tell them you utilized the Hadoop framework because of its scalability and ability to increase the data processing speed while preserving the quality.

Some features of Hadoop include: 

• It is Java-Based. Hence, there may be no additional training required for team members. Also, it is easy to use. 
• As the data is stored within Hadoop, it is accessible in the case of hardware failure from other paths, which makes it the best choice for handling big data. 
• In Hadoop, data is stored in a cluster, making it independent of all the other operations.

In case you have no experience with this tool, learn the necessary information about the tool's properties and attributes.

24. Can you tell me about NameNode? What happens if NameNode crashes or comes to an end?

It is the centre-piece or central node of the Hadoop Distributed File System(HDFS), and it does not store actual data. It stores metadata. For example, the data being stored in DataNodes on which rack and which DataNode the information is stored. It tracks the different files present in clusters. Generally, there is one NameNode, so when it crashes, the system may not be available.

25. Are you familiar with the concepts of Block and Block Scanner in HDFS?

You'll want to answer by describing that Blocks are the smallest unit of a data file. Hadoop automatically divides huge data files into blocks for secure storage. Block Scanner validates the list of blocks presented on a DataNode.

26. What happens when Block Scanner detects a corrupted data block?

It is one of the most typical and popular interview questions for data engineers. You should answer this by stating all steps followed by a Block scanner when it finds a corrupted block of data. 

Firstly, DataNode reports the corrupted block to NameNode.NameNode makes a replica using an existing model. If the system does not delete the corrupted data block, NameNode creates replicas as per the replication factor. 

27. What are the two messages that NameNode gets from DataNode?

NameNodes gets information about the data from DataNodes in the form of messages or signals. 

The two signs are:

• Block report signals which are the list of data blocks stored on DataNode and its functioning.
• Heartbeat signals that the DataNode is alive and functional. It is a periodic report to establish whether to use NameNode or not. If this signal is not sent, it implies DataNode has stopped working.

28. Can you elaborate on Reducer in Hadoop MapReduce? Explain the core methods of Reducer?

Reducer is the second stage of data processing in the Hadoop Framework. The Reducer processes the data output of the mapper and produces a final output that is stored in HDFS. 

The Reducer has 3 phases:

• Shuffle: The output from the mappers is shuffled and acts as the input for Reducer.
• Sorting is done simultaneously with shuffling, and the output from different mappers is sorted. 
• Reduce: in this step, Reduces aggregates the key-value pair and gives the required output, which is stored on HDFS and is not further sorted.

There are three core methods in Reducer:

• Setup: it configures various parameters like input data size.
• Reduce: It is the main operation of Reducer. In this method, a task is defined for the associated key.
• Cleanup: This method cleans temporary files at the end of the task.

29. How can you deploy a big data solution?

While asking this question, the recruiter is interested in knowing the steps you would follow to deploy a big data solution. You should answer by emphasizing on the three significant steps which are:

• Data Integration/Ingestion: In this step, the extraction of data using data sources like RDBMS, Salesforce, SAP, MySQL is done.
• Data storage: The extracted data would be stored in an HDFS or NoSQL database.
• Data processing: the last step should be deploying the solution using processing frameworks like MapReduce, Pig, and Spark.

30. Which Python libraries would you utilize for proficient data processing?

This question lets the hiring manager evaluate whether the candidate knows the basics of Python as it is the most popular language used by data engineers. 

Your answer should include NumPy as it is utilized for efficient processing of arrays of numbers and pandas, which is great for statistics and data preparation for machine learning work. The interviewer can ask you questions like why would you use these libraries and list some examples where you would not use them.

31. Can you differentiate between list and tuples?

Again, this question assesses your in-depth knowledge of Python. In Python, List and Tuple are the classes of data structure where Lists are mutable and can be edited, but Tuples are immutable and cannot be modified. Support your points with the help of examples.

32. How can you deal with duplicate data points in an SQL query?

Interviewers can ask this question to test your SQL knowledge and how invested you are in this interview process as they would expect you to ask questions in return. You can ask them what kind of data they are working with and what values would likely be duplicated? 

You can suggest the use of SQL keywords DISTINCT & UNIQUE to reduce duplicate data points. You should also state other ways like using GROUP BY to deal with duplicate data points.

33. Did you ever work with big data in a cloud computing environment?

Nowadays, most companies are moving their services to the cloud. Therefore, hiring managers would like to understand your cloud computing capabilities, knowledge of industry trends, and the future of the company's data. 

You must answer it stating that you are prepared for the possibility of working in a virtual workspace as it offers many advantages like:

• Flexibility to scale up the environment as required, 
• Secure access to data from anywhere
• Having backups in case of an emergency

34. How can data analytics help the business grow and boost revenue?

Ultimately, it all comes down to business growth and revenue generation, and Big Data analysis has become crucial for businesses. All companies want to hire candidates who understand how to help the business grow, achieve their goals, and result in higher ROI. 

You can answer this question by illustrating the advantages of data analytics to boost revenue, improve customer satisfaction, and increase profit. Data analytics helps in setting realistic goals and supports decision making. By implementing Big Data analytics, businesses may encounter a 5-20% significant increase in revenue. Walmart, Facebook, LinkedIn are some of the companies using big data analytics to boost their income.

35. Define Hadoop Streaming.

Hadoop Streaming is a feature or utility included with a Hadoop distribution that lets programmers or developers construct Map-Reduce programs in many programming languages such as Python, Ruby, C++, Perl, and others. We may leverage any language capable of reading from STDIN (standard input), such as keyboard input, and write to STDOUT (standard output).

36. What is the full form of HDFS?

The full form of HDFS is Hadoop Distributed File System.

37. List out various XML configuration files in Hadoop.

The following are the various XML configuration files in Hadoop:

• HADOOP-ENV.sh
• CORE-SITE.XML
• HDFS-SITE.XML
• MAPRED-SITE.XML

38. What are the four v’s of big data?

The four V's of Big Data are Volume, Velocity, Variety, and Veracity.

39. Explain the features of Hadoop.

Some of the most important features of Hadoop are:

• It's open-source: It is an open-source project, which implies that its source code is freely available for modification, inspection, and analysis, allowing organisations to adapt the code to meet their needs.
• It offers fault tolerance: Hadoop's most critical feature is fault tolerance. To achieve fault tolerance, Hadoop 2's HDFS employs a replication strategy. Based on the replication, it beautifully makes a clone of every block on each system (by default, it’s 3). As a result, if any machine in a cluster fails, data may be accessed from other devices that carry duplicates of the same data.
• It is highly scalable: To reach high computing power, the Hadoop cluster is highly scalable, which means we may add any amount of nodes or expand the hardware potential of nodes. This gives the Hadoop architecture horizontal as well as vertical scalability.

40. What is the abbreviation of COSHH?

COSHH stands for Control of Substances Hazardous to Health.

41. Explain Star Schema.

A Star Schema is basically a multi-dimensional data model that is used to arrange data in a database so that it may be easily understood and analysed. Data marts, Data Warehouses , databases, and other technologies can all benefit from star schemas. The star schema style is ideal for querying massive amounts of data.

42. Explain FSCK

FSCK, an acronym for File System Consistency Checker, is one method older Linux-based systems still employ to detect and correct errors. It is not a comprehensive solution, as inodes pointing to junk data may still exist. The primary goal is to ensure that the metadata is internally consistent.

43. Explain Snowflake Schema.

A snowflake schema is basically a multidimensional database schema that divides subdimensions into dimension tables. Engineers convert every dimension table into logical subdimensions while designing a snowflake schema. As a result, the data model turns out to be more complicated, but it might also make it straightforward for analysts in dealing with it, particularly for certain data kinds. Because its ERD (entity-relationship diagram) resembles a snowflake, it is known as the "snowflake schema."

44. Distinguish between Star and Snowflake Schema.

The following are some of the distinguishing features of a StE Schema and a Snowflake Schema:

• The star schema is the most basic kind of Data Warehouse schema. It's referred to as the star schema as its structure is similar to a star's. A Snowflake Schema is an expansion of a Star Schema that adds dimension. It's called a snowflake, as the diagram appears to be like a snowflake.
• Only a single join describes the link between any dimension table and the fact table in a star schema. A fact table is enveloped by dimension tables in the star schema, whereas a snowflake schema is surrounded by dimension tables, which are surrounded by dimension tables, and so forth. To get data from a snowflake schema, numerous joins are required.

45. Explain Hadoop Distributed File System.

Hadoop applications use HDFS (Hadoop Distributed File System) as their primary storage system. This open-source framework operates by passing data between nodes as quickly as possible. Companies that must process and store large amounts of data frequently employ it. HDFS is a critical component of many Hadoop systems since it allows for managing and analysing large amounts of data.

46. What Is the full form of YARN?

The full form of YARN is Yet Another Resource Negotiator.

47. List various modes in Hadoop.

There are three different types of modes in Hadoop:

• Fully-Distributed Mode
• Pseudo-Distributed Mode
• Standalone Mode

48. How to achieve security in Hadoop?

Apache Hadoop offers users security in the following ways:

• Kerberos was implemented using SASL/GSSAPI. It is also used on RPC connections to mutually validate users, their procedures, and Hadoop services.
• Delegation tokens are used in connection with the NameNode for future authenticated access that does not need the usage of the Kerberos Server.
• Web application and web console developers might create their own HTTP authentication method, including HTTP SPNEGO authentication.

49. What Is Heartbeat in Hadoop?

A heartbeat in Hadoop is a signal sent from the Datanode to the Namenode, indicating it is alive. In HDFS, the lack of a heartbeat signals a problem, and the Namenode and Datanode cannot do any computations.

50. Distinguish between NAS and DAS in Hadoop.

The following are some of the differences between NAS (Network Attached Storage) and DAS (Direct Attached Storage):

• The computing and storage layers are separated in NAS. Storage is dispersed among several servers in a network. Storage is tied to the node where computing occurs in DAS.
• Apache Hadoop is founded on the notion of bringing processing close to the data. As a result, the storage disc must be close to the calculation. DAS provides excellent performance on a Hadoop cluster. DAS can also be implemented on common hardware. As a result, it is less expensive when compared to NAS.

51. List important fields or languages used by data engineers.

Scala, Java, and Python are some of the most sought-after programming languages that are leveraged by data engineers.

52. What is Big Data?

Big data refers to huge, complicated data sets that are created and sent in real-time from a wide range of sources. Big data collections can be organised, semi-structured, or unstructured, and they are regularly examined to uncover relevant patterns and insights regarding user and machine behaviour.

53. What is FIFO Scheduling?

FCFS, or First Come First Service, is basically an operating system scheduling method that performs queued processes and requests in the order in which they arrive. It's the most straightforward and basic CPU scheduling technique. Processes seeking the CPU first receive the CPU allocation in this method. A FIFO queue is leveraged to handle this.

54. Mention default port numbers on which the task tracker, NameNode, and job tracker run in Hadoop.

• Task Tracker: 50060
• NameNode: 50070
• JobTracker: 50030

55. How to define the distance between two nodes in Hadoop?

The network is represented as a tree in Hadoop. The distance between two nodes is the total of their ancestor distances.

56. Why use commodity hardware in Hadoop?

The idea behind using Commodity Hardware in Hadoop is simple: you've got a few servers and distribute the load among them. It is possible because of Hadoop MapReduce, a fantastic component of this setup.

In Hadoop, commodity hardware is put on all servers, and it then distributes data across them. Every server will eventually hold a piece of data. No server, however, will contain everything.

57. Define Replication Factor in HDFS.

The replication factor specifies the number of copies of a block that should be stored in your cluster. Because the replication factor is set to three by default, every file you create in the Hadoop Distributed File System will be having a replication factor of three, and each block in the file will be duplicated to three distinct nodes in your cluster.

58. What data is stored in NameNode?

NameNode serves as the master of the system. It monitors the metadata and file system tree for every folder and file on the system. The information of metadata is saved in two files: 'Edit Log' and 'Namespace image'.

59. What do you mean by Rack Awareness?

Rack awareness in Hadoop refers to recognising how various data nodes are dispersed across racks or knowing the cluster architecture in the Hadoop cluster.

60. What are the functions of Secondary NameNode?

The following are some of the functions of the secondary NameNode:

• Keeps a copy of the FsImage file and an edit log.
• Apply edits log entries to the FsImage file on a regular basis and renews the edits log. It then sends this modified FsImage file directly to NameNode so that it does not have to re-address the EditLog records at the time of the startup process. As a result, Secondary NameNode speeds up the NameNode startup procedure.
• If NameNode fails, File System information can be retrieved from the last stored FsImage on the Secondary NameNode, however, the Secondary NameNode cannot take over the functionality of the Primary NameNode.
• File system information is checked for accuracy.

61. What are the basic phases of reducer in Hadoop?

A Reducer in Hadoop has three major phases:

• Shuffle: Reducer duplicates the sorted output from each Mapper during this step.
• Sort: During this stage, the Hadoop framework sorts the input to the Reducer by the same key. This step employs merge sort. Sometimes the shuffle and sort processes occur concurrently.
• Reduce: It's the stage at which the output values associated with a key are lowered to produce an output result. Reducer output is not re-sorted.

62. Why does Hadoop use Context objects?

Context object permits the Mapper/Reducer to communicate with the remainder of the Hadoop system. It contains task configuration data and interfaces that allow it to send output. It can be used by programs to report progress.

63. Define Combiner in Hadoop.

The Combiner, also known as the "Mini-Reducer," summarises the Mapper output record using the same Key before handing it to the Reducer.

When we execute a MapReduce task on a huge dataset. As a result, Mapper creates vast amounts of intermediate data. The framework then forwards this intermediate data to the Reducer for further processing.

This causes massive network congestion. The Hadoop framework has a function called Combiner, which helps to reduce network congestion.

Combiner, sometimes known as a "Mini-Reducer," is responsible for processing the output data from the Mapper before transferring it to the Reducer. It is executed after the mapper but before the reducer. Its application is discretionary.

64. What is the default replication factor available in HDFS? What does it indicate?

HDFS's replication factor is set to 3 by default. This implies that each block will have two additional copies stored on a different DataNode in the cluster.

65. What do you mean by Data Locality in Hadoop?

Data locality in Hadoop brings computation near where the real data is on the node rather than transporting massive data to computation. It lowers network congestion while increasing total system throughput.

66. Define Balancer in HDFS.

The HDFS Balancer is basically a utility for balancing data across an HDFS cluster's storage devices. The HDFS Balancer was initially designed to run slowly so that balancing operations did not interfere with regular cluster activity and job execution.

67. Explain Safe Mode in HDFS.

The Hadoop Distributed File System (HDFS) cluster's safe mode for the NameNode is read-only. You cannot change or block the file system while in Safe Mode. When the DataNodes indicate that most file system blocks are accessible, the NameNode exits Safe Mode automatically.

68. What is the importance of Distributed Cache in Apache Hadoop?

In Hadoop, the distributed cache is a method of copying archives or small files to worker nodes in real time. This is done by Hadoop so that these worker nodes may use them when conducting a job. To conserve network traffic, files are copied just once per job.

70. What is Metastore in Hive?

Metastore in Hive is the component that maintains all of the warehouse's structural information, including serializers and deserializers required to read and write data, column and column type information, and the accompanying HDFS files where the data is kept.

71. What do you mean by SerDe in Hive?

Athena communicates with data in multiple forms via a SerDe (Serializer/Deserializer). The SerDe you provide defines the table schema, not the DDL. In other words, the SerDe can overrule the DDL settings you give when you create your table in Athena.

72. List the components available in the Hive data model.

The following components are included in Hive data models:

• Clusters or buckets

73. Explain the use of Hive in the Hadoop ecosystem.

Hive is a data warehousing and an ETL solution for querying and analysing massive datasets stored in the Hadoop environment. Hive has three essential purposes in Hadoop: data summarisation, querying and analysing unstructured and semi-structured data.

74. List various complex data types/collections supported by Hive.

The complex data collections or types supported by Hive are as follows:

75. Explain how the .hiverc file in Hive is used.

The initialisation file is called hiverc. This file is loaded when we launch the Command Line Interface for Hive. In this file, we may specify the starting values of parameters.

76. Is it possible to create multiple tables in Hive for a single data file?

Hive permits you to write data to numerous tables or folders simultaneously.

77. Explain different SerDe implementations available in Hive.

• For IO, Hive employs the SerDe interface. The interface supports both serialization and deserialization, as well as serialisation results as separate fields for processing.
• Hive can read data from a table and also write it back to the Hadoop Distributed File System in any personalised format using a SerDe. Any individual with a computer may create their SerDe for their data types.

78. List table-generating functions available in Hive.

The table-generating functions that are available in Hive are as follows:

• explode(ARRAY)
• explode(MAP)
• inline(ARRAY<STRUCT[,STRUCT]>)
• explode(array a)
• json_tuple(jsonStr, k1, k2, …)
• parse_url_tuple(url, p1, p2, …)
• posexplode(ARRAY)
• stack(INT n, v_1, v_2, …, v_k)

79. What is a Skewed table in Hive?

A Skewed table in Hive has values in considerable quantities compared to other data. The Skew data is kept in a separate file, while the remainder is kept in another.

80. List objects created by CREATE statements in MySQL.

Using the CREATE statement, the following objects are created:

81. How to see the database structure in MySQL?

To display the database structure and its properties in MySQL, you need to use the DESCRIBE function:

DESCRIBE table_name; OR DESC table_name;.

82. How to search for a specific String in the MySQL table column?

The location of the first occurrence of a string within a string is returned by MySQL LOCATE(). These strings are both supplied as arguments. An optional parameter can be used to determine where the search should begin in the string (i.e. the text to be searched).

83. Explain how data analytics and big data can increase company revenue.

Big data analytics enables businesses to develop new goods based on consumer demands and preferences. Because these things help organisations to generate more money, firms are turning to big data analytics. Big data analytics may help businesses raise their income by 5-20%. Furthermore, it allows businesses to understand their competitors better.

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The Microsoft Data Engineer Interview Guide (Updated for 2024)

Back to Microsoft

Introduction

Microsoft has been pushing boundaries in tech for a while now. Apart from their recent bold foray into AI, they are expected to continue to make strides in their businesses ranging from gaming to cloud computing. All this innovation means that they need to recruit top talent, and that’s why they need more data engineers in 2024.

With attractive pay, good work-life balance, and great health benefits, Microsoft is a generous and flexible employer.

In this detailed guide, we’ll demystify the Microsoft Data Engineer interview process for you. Most importantly, we’ll cover a wide range of questions that are popularly asked in Microsoft interviews and give you tips on tackling them like a pro.

What is the Interview Process Like for a Data Engineer Role at Microsoft?

This role will test your expertise in data modeling, coding, and, most importantly, problem-solving. Further, they want engineers who can communicate well and demonstrate passion and curiosity. Cultural fit is also important, so make sure to prepare responses for common behavioral questions.

Please note that the questions and structure of the interview process will differ based on the team and function advertised in the job description. Always go through the job role carefully  while preparing your interview strategy.

Microsoft’s interview process can take anywhere between 4 weeks to 3 months.

Step 1:  Preliminary Screening

A recruiter would call you to get a sense of your work experience and your cultural fit. They may ask you why you want to join Microsoft and a couple of questions related to past projects, so prepare some canned responses based on your research about the company and past projects.

Step 2: Technical Assessment

Successful candidates then undergo one or two technical interviews, usually via video chat. This is often a live coding round on a shared whiteboard. You may be asked to demonstrate your engineering knowledge through scenario-based case studies as well.

Step 3: Onsite Interviews

If it’s a good fit, you will be invited onsite to meet your team and have a few rounds of interviews. These typically involve a mix of technical, behavioral, and case study questions. You can expect an entire round devoted to architecture and design questions.

Step 4: Final Interview

The final stage usually involves meeting with senior-level executives or team leaders. This last round will assess your cultural fit and your motivation to join the firm.

What Questions Are Asked in a Microsoft Data Engineer Interview?

Microsoft’s data engineering interview questions primarily focus on practical skills in data manipulation, query optimization, and algorithm design, along with problem-solving in real-world data engineering scenarios. The questions are designed to test technical expertise, analytical thinking, and the ability to apply knowledge in practical situations relevant to Microsoft’s data engineering challenges.

For a more in-depth look at these questions, let’s go through the list we have below:

1. Given a list of integers, write a function to find the greatest common denominator between them.

This question assesses your understanding of basic algorithms and number theory, both fundamental for Microsoft data engineers who need to manipulate and analyze large datasets.

How to Answer  

Explain the concept of GCD (Greatest Common Denominator) as the largest number that divides all integers in the list evenly. Choose an efficient algorithm, like the Euclidean algorithm, and outline its steps. Briefly mention how you would handle edge cases like empty lists or negative numbers.

“The greatest common denominator (GCD) of a list of integers is the largest number that all the elements are divisible by. An effective approach would be the Euclidean algorithm, which repeatedly divides the larger number by the smaller until the remainder is 0. The final divisor is the GCD.

It’s important to consider edge cases like empty lists, where the GCD wouldn’t be defined. We can handle this by setting the GCD to 0 for an empty list. Additionally, we need to ensure the algorithm works for negative numbers: simply converting all negatives to positives before applying the Euclidean algorithm can address this.”

2. Let’s say we have a table with ‘ID’ and ‘name’ fields. The table holds over 100 million rows, and we want to sample a random row in the table without throttling the database. Write a query to randomly sample a row from this table.

At Microsoft, engineers need to be able to efficiently query and sample from large datasets. This question assesses your ability to optimize queries considering Microsoft’s platform functionalities and performance priorities.

Briefly mention how full table scans can be detrimental to database performance, especially when dealing with millions of rows. Highlight one or two approaches like OFFSET with random numbers and reservoir sampling (Knuth’s algorithm). Briefly explain their core concepts and suitability for the given scenario, and finally, choose the method you think is most optimal for Microsoft’s context.

“Given its alignment with efficient database querying and its utilization of standard SQL functions, I’d prioritize the OFFSET method for Microsoft’s context. However, if memory constraints are tighter, reservoir sampling could be a valuable alternative.”

3. We have a table representing a company payroll schema. Due to an ETL error, the employees table, instead of updating the salaries when doing compensation adjustments, did an insert instead. The head of HR still needs the salaries. Write a query to get the current salary for each employee.

Troubleshooting and fixing such data issues will be part of your day-to-day as a data engineer at Microsoft.

How to Answer

Mention the use of SQL constructs like subqueries, window functions, or GROUP BY clauses. Your explanation should demonstrate your ability to write efficient SQL queries.

“To get the current salary for each employee from the payroll table, I would use ROW_NUMBER() over a partition of the employee ID, ordered by the salary entry date in descending order. This ordering ensures that the most recent entry has a row number of 1. I would then wrap this query in a subquery or a Common Table Expression (CTE) and filter the results to include only rows where the row number is 1. This method ensures that only the latest salary entry for each employee is retrieved, correcting the ETL error that caused multiple inserts.”

4. Given n dice, each with m faces, write a function to make a list of all possible combinations of dice rolls. Can you also do it recursively?

Microsoft Data Engineers frequently handle tasks involving simulations or probabilistic calculations, for example, while creating scalable data processing solutions. This question is designed to see how you translate complex logical problems into implementable code.

Define the function and briefly describe your approach, for example, to utilize built-in functions to efficiently generate all individual face value combinations and yield each as a tuple. It’s also beneficial to mention the trade-offs between iterative and recursive solutions in terms of readability and performance.

“For massive datasets, efficiency should be prioritized with an iterative approach like the product function. It will generate all individual face value combinations and yields them as tuples, minimizing runtime and maximizing performance.”

5. Imagine you’re designing a product for Slack called “Slack for School.” What are the critical entities, and how would they interact? Imagine we want to provide insights to teachers about students’ class participation. How should we design an ETL process to extract data on student interaction with the app ?

You need to be able to design data-driven solutions for such real-world scenarios. A data engineer at Microsoft will have to understand user requirements, conceptualize data structures, and create efficient extraction and transformation processes.

Your answer should clearly outline your understanding and scope of a “Slack for School” environment and the role of data in enhancing its functionality. Ask clarifying questions to identify critical entities, and make sure you state your assumptions clearly.

Example Answer:

“In “Slack for School,” the critical entities would include students, teachers, classes, messages, and participation metrics. Students and teachers interact through messages within classes. To provide insights into students’ participation, we would need an ETL process that extracts data on message frequency, response times, and interaction types (like questions, answers, or comments). This data would be extracted from Slack’s API, transformed to categorize and quantify participation levels, and loaded into a data warehouse where teachers can access summarized reports. This process ensures that teachers receive meaningful and actionable insights about student engagement in their classes.”

6. Write a function that takes a sentence or paragraph of strings and returns a list of all its bigrams in order.

Microsoft data engineers often deal with large volumes of textual data. This question tests your NLP (Natural Language Processing) skills, which are essential for text analysis.

Explain your method for generating bigrams, emphasizing efficiency and scalability, which are crucial in dealing with large datasets— common in Microsoft’s environment.

“I would write a function that splits the input text into individual words (tokens). This is typically done using the split() function in Python, which splits the text based on spaces. Once we have a list of words, the function iterates through this list to create bigrams.”

7. Let’s say you have analytics data stored in a data lake. An analyst tells you they need hourly, daily, and weekly active user data for a dashboard that refreshes every hour. How would you build this data pipeline?

This question is relevant in a Microsoft data engineer interview because it assesses your ability to design a data pipeline tailored to meet specific business requirements. It also tests your understanding of data processing and scheduling, which are critical skills in engineering teams.

Explain your approach to handling time-series data, ensuring data accuracy and timeliness, and selecting appropriate tools. Mention how you would schedule and automate the pipeline to refresh data.

“I would use Azure Data Factory to pull user data from the lake. The transformation stage could be efficiently handled using a distributed processing system like Azure Databricks. For the loading stage, I would use Azure Synapse Analytics to store the transformed data, as it allows for the quick retrieval of data, which is essential if we need to refresh it every hour. Additionally, I would implement monitoring and logging to track the pipeline’s performance and quickly address any issues.”

8. Write a SQL query to create a histogram of the number of comments per user in a particular month.

Microsoft’s ecosystem often involves analyzing user interactions on platforms like LinkedIn, Xbox, or Microsoft Teams. The question tests the ability to create actionable insights from data; a key responsibility in engineering roles.

Focus on the use of date functions and the aggregation method you would use. Relate your answer to a scenario relevant to Microsoft’s products or services where analyzing user engagement is important, such as in understanding platform usage patterns.

“In the query, I would filter the records based on the comment_date *to include only those from the relevant month. I would then group the data by user_id and count the number of comments for each user.”

9. Let’s say you have a table with a billion rows. How would you add a column inserting data from the source without affecting the user experience?

This question assesses your expertise in large-scale data operations in a live environment. Microsoft’s ecosystem often involves handling big datasets in products like Azure SQL Database or services that power platforms like LinkedIn.

Focus on methods like partitioning, batch processing, and leveraging features unique to Microsoft’s database systems. Discuss the importance of planning, testing in a non-production environment, and monitoring the impact on performance.

“I would first test the process in a controlled environment, like a replica of the production database in Azure SQL Database or SQL Server. I would utilize batch processing to add the column, dividing the operation into smaller, manageable parts. Conducting the update during off-peak hours is essential to minimize the impact on users, especially for services with global reach.”

10. How would you diagnose and optimize a slow-running SQL query in a Microsoft SQL Server environment?

This question is popular in Microsoft data engineer interviews because it assesses your SQL expertise and performance-tuning skills. Optimizing slow-running queries is essential to maintain database performance in large-scale environments typical at Microsoft.

Your answer should demonstrate a systematic approach to diagnosing SQL queries. Mention the use of techniques like query execution plans, indexing, and SQL Server performance counters.

“I would analyze the query execution plan to identify bottlenecks, such as full table scans or inefficient joins. Next, I’d add or optimize indexing to improve retrieval efficiency. Simplifying the query by reducing unnecessary joins and subqueries can also be effective. I would review SQL Server performance counters to ensure the issue isn’t related to broader server-level problems like CPU, memory, or disk I/O constraints.”

11. Imagine we’re migrating a legacy database to Azure SQL Database. What steps would you take to ensure a smooth transition, and how would you handle potential data inconsistencies?

This type of experience as a data engineer would be extremely valuable as Microsoft would have ongoing projects involving migration from legacy systems to cloud-based ones. It is imperative to ensure minimal disruption to enterprise-scale applications.

Include pre-migration assessment, migration planning, execution, and post-migration validation in your proposed plan. Mention how you’d deal with inconsistencies and discuss methods to minimize downtime during migration.

“I’d start with a thorough assessment to identify compatibility and data format issues. The planning phase would involve selecting appropriate migration tools like Azure Database Migration Service and strategizing for data consistency checks. During execution, I’d use parallel processing for minimal downtime and implement validation checks for data integrity. I’d focus on extensive testing after completing the migration to ensure full functionality and accuracy.”

12. If you’re working on integrating on-premises data storage with Azure cloud services, what factors would you consider, and what approach would you take for a seamless integration?

This question is pertinent in a Microsoft data engineer interview to assess your understanding of hybrid cloud solutions, a key area in Microsoft’s cloud strategy. It’s relevant for integrating legacy systems with Azure services for Microsoft’s diverse range of clients so that they can leverage cloud scalability while also retaining on-premise systems.

Your answer should cover the considerations for integration, such as data security, network connectivity, compliance, and synchronization. Also, discuss the use of specific Azure tools to facilitate this integration.

“Key considerations would include secure network connectivity, which can be achieved through Azure ExpressRoute. I would use Azure Security Center to align with security standards and regulations. For data synchronization, Azure Data Factory can create a seamless data movement pipeline. I’d also ensure the architecture supports elastic scaling to handle variable workloads.”

13. You have a database table Sales in SQL Server, containing columns ProductId , SaleDate , Region , and Amount . The table has millions of rows. You are asked to write an SQL query to find the total sales amount for each product by region for the last quarter, sorted in descending order. How would you write and optimize this query?

The ability to navigate big datasets and write efficient queries is crucial for supporting decision-making processes quickly, for instance, when another team is relying on Microsoft engineers to provide data promptly.

Mention using WHERE clauses for filtering data to the last quarter and GROUP BY for aggregation. It’s important to demonstrate your advanced understanding of query optimization, so briefly discuss indexing strategies to improve performance.

“I would ensure the SaleDate , ProductId , and Region columns are indexed as indexing is key in speeding up query execution. The query would contain WHERE , GROUP BY , and ORDER BY clauses to meet the requirement of sorting by total sales amount.”

14. Let’s say that you need to analyze a large JSON file containing log data from a web application. Describe the steps and the Python libraries you would use to read, process, and aggregate the data. Also, explain how you would handle any data anomalies.

Microsoft, with its vast array of web-based services and applications, often requires engineers to work with complex data formats like JSON. The question tests skills in parsing, processing, and analysis using Python, a key language used by engineers.

It’s always best to understand the business use case first to determine the scope of the problem. Then, lay out your solution while clearly stating any assumptions you’ve made.

“I would use the Python json library to parse the file. For large files, I might consider ijson or pandas with read_json , which can handle JSON data in a more memory-efficient manner. I would then use pandas for data processing and aggregation, as well as to filter, clean, or fill in any missing values. For example, missing values could be filled with averages or median values, or I could use more sophisticated imputation techniques depending on the business use case. Additionally, I would implement checks for outliers using functions from Scipy or numpy .”

15. You are given a table CustomerData with columns CustomerID , Name , Email , and SignUpDate . You notice that some of the email addresses are invalid, and some names contain extra whitespace characters. You are asked to write an SQL script to clean these anomalies. Also, explain how you would identify and handle any other potential data quality issues in this table.

In a Microsoft environment, maintaining high data quality is crucial for engineers to facilitate customer relationship management, marketing, and analytics, and this question tests this ability.

Discuss the use of SQL functions for string manipulation and validation. Also, talk about general data quality checks you would perform on the table and how you would rectify identified issues.

“For email validation, I would use a combination of string functions and a regular expression to flag invalid formats. For the names, functions like TRIM can be used to remove extra whitespaces. Beyond these specific issues, I would look for common data quality problems such as null or missing values, duplicates, and inconsistent data entries.”

16. What is the Hadoop Distributed File System (HDFS)? How does it differ from a traditional file system?

Microsoft’s Azure HDInsight service integrates with Hadoop, making knowledge of HDFS crucial for engineers to handle large-scale processing and storage in a distributed computing environment.

Your answer should focus on aspects like scalability, fault tolerance, data distribution, and how HDFS manages large datasets.

“Unlike traditional file systems, HDFS spreads data across many nodes, allowing it to handle petabytes of data. HDFS is highly fault-tolerant; it stores multiple copies of data (replicas) on different machines, ensuring that data is not lost if a node fails. It is designed to work with commodity hardware, making it cost-effective for handling massive amounts of data. HDFS is tightly integrated with the MapReduce programming model, allowing for efficient processing.”

17. How would you implement a binary search algorithm?

Having a good understanding of basic concepts like binary search is imperative. A data engineer at Microsoft will need to use this algorithm a lot of the time to quickly retrieve data from sorted datasets.

Describe the binary search algorithm, emphasizing its efficiency and the conditions under which it operates (e.g., the data must be sorted). Explain the step-by-step process of dividing the search interval in half and how the search space is reduced at each step.

“I would start by identifying the low and high boundaries of the array (or list) containing the data. The algorithm then enters a loop where it calculates the midpoint of the low and high boundaries. If the element at the midpoint is equal to the target value, the search is successful and the index is returned. If the target value is less than the element at the midpoint, the algorithm repeats for the lower half of the array (adjusting the high boundary). If the target value is greater, it repeats for the upper half (adjusting the low boundary). This process continues until the element is found or the low boundary exceeds the high boundary, indicating the element is not in the array. Binary search is efficient with a time complexity of O(log n), making it suitable for large datasets.”

18. Why do you want to join Microsoft?

Microsoft wants to hire individuals who are passionate about what the company stands for. Interviewers will want to know why you specifically chose to apply for the Data Engineer role at Microsoft and whether you have done your research properly.

Your answer should cover why you chose the company and role and why you’re a good match for both. Try to frame your answer positively and honestly. Additionally, focus on the value you’ll bring to the organization.

“I want to work for Microsoft because I am deeply inspired by its commitment to innovation and its role in shaping the future of technology, particularly in cloud computing and AI. The company’s culture of diversity and inclusion aligns with my values.

“I bring a blend of technical proficiency and a passion for data-driven problem-solving. Additionally, my collaborative approach and experience in diverse teams will ensure that I’m a great fit.”

19. Tell me about a time you failed.

As a data engineer, you’ll make mistakes from time to time. In a collaborative culture like Microsoft’s, they’ll want to know if you’re able to be open about mistakes and utilize the learnings to continuously improve and educate.

Familiarize yourself with the STAR (Situation, Task, Action, Result) method to structure your response in an organized manner.

When answering this question, especially in the context of Microsoft’s open and collaborative culture, it’s important to be honest and reflective. Choose a real example of a professional error, describe what happened, and most importantly, what you learned and how it shaped your growth. Emphasize how you took responsibility and how this experience has changed your approach to challenges and teamwork.

“In my previous role, I was tasked with optimizing a complex ETL process that was taking too long to complete. I proposed a series of performance improvements based on my analysis. However, when I implemented them, the process crashed, causing a significant data outage. It was a critical failure, and I had to work tirelessly with the team to resolve the issue.

This experience taught me the importance of thorough testing and monitoring during any system optimization. I also learned the value of communication with the team and stakeholders, keeping them informed of progress and setbacks.”

20. Can you describe a situation where you had to collaborate with a difficult team member?

The interviewer needs to understand how you handle conflicts in a team setting, as data engineering often requires close collaboration with various teams in Microsoft.

Use the STAR method of storytelling - discuss the S pecific situation you were challenged with, the T ask you decided on, the A ction you took, and the R esult of your efforts. Make sure to quantify impact when possible.

“In a past project, I worked with a team member who tended to make unilateral decisions and had difficulty effectively communicating their thought process.

Realizing this was affecting our productivity and team dynamics, I requested a private meeting with this colleague. I aimed to understand their perspective while expressing the team’s concerns in a constructive way. During our conversation, I learned that their approach stemmed from a deep sense of responsibility and a fear of project failure. I acknowledged their commitment and then elaborated on how collaborative decision-making could enhance project outcomes.

We agreed on a more collaborative approach, with regular briefings where updates were clearly outlined. This experience taught me the value of addressing interpersonal challenges head-on, but with empathy. The situation improved significantly after our discussion.”

How to Prepare for a Data Engineer Interview at Microsoft

Here are some tips to help you excel in your interview.

Study the Company and Role

Research the role, team, and company.

Research recent news, updates, Microsoft values, and business challenges the company is facing. Understanding the company’s culture and strategic goals will allow you to not only present yourself better but also understand if they are a good fit for you.

Additionally, review the job description carefully. Tailor your preparation to the specific requirements and technologies mentioned in the job description.

You can also read Interview Query members’ experiences on our  discussion board  for insider tips and first-hand information.

Brush Up on Technical Skills

Make sure you have a strong foundation in programming languages like Python, Java, and Scala, as well as SQL and data structures. Familiarity with cloud computing platforms like Azure is also a plus.

Check out the resources we’ve tailored for data engineers: a case study guide , a compendium of data engineer interview questions, data engineering projects to add to your resume, and a list of great books to help you on your engineering journey. If you need further guidance, you can consider our tailored data engineering learning path as well.

Prepare Behavioral Interview Answers

Soft skills such as collaboration, effective communication, and problem-solving are paramount to succeeding in any job, especially in a collaborative culture such as Microsoft’s.

To test your current preparedness for the interview process, try a  mock interview  to improve your communication skills.

Keep Up With The Industry

The data engineering landscape is constantly evolving, so keep yourself updated on the latest technologies, news, and best practices.

Network With Employees

Connect with people who work at Microsoft through LinkedIn or other online platforms. They can provide valuable insights into the company culture and the interview process.

Consider checking our complete Data Engineer Prep Guide to make sure that you don’t miss anything important while preparing for your interview at Microsoft.

What is the average salary for a Data Engineer role at Microsoft?

Average Base Salary

Average Total Compensation

View the full Data Engineer at Microsoft salary guide

The average base salary for a Data Engineer at Microsoft is  $135,221 , making the remuneration considerably higher than that of the average data engineering role in the US.

For more insights into the salary range of a data engineer at various companies, segmented by city, seniority, and company, check out our comprehensive  Data Engineer Salary Guide .

Where can I read more discussion posts on the Microsoft Data Engineer role here on Interview Query?

Here is our  discussion board , where our members talk about their Microsoft interview experience. You can also use the search bar to look up data engineer interview experiences in other firms to gain more insight into interview patterns.

Are there job postings for Microsoft Data Engineer roles on Interview Query?

We have jobs listed for data engineer roles in Microsoft, which you can apply for directly through our  job portal . You can also have a look at similar roles that are relevant to your career goals and skill set.

In conclusion, succeeding in a Microsoft Data Engineer interview requires not only a strong foundation in technical skills and problem-solving but also the ability to work in a collaborative environment.

If you’re considering opportunities at other tech companies, check out our  Company Interview Guides . We cover a range of companies, including  Google ,  IBM , Apple , and more.

For other data-related roles at Microsoft, consider exploring our guides for  Business Analyst ,  Data Analyst ,  Scientist , and  Software Engineer positions  in our  main Microsoft interview guide .

If you’re looking for a broader set of information about interview questions for data engineers, then you can look through our main data engineering interview guide , case studies , as well as our Python and SQL sections .

Understanding Microsoft’s culture of innovation and collaboration and preparing thoroughly with both technical and behavioral questions is the key to your success.

Check out more of Interview Query’s content, and we hope you’ll land your dream role at Microsoft very soon!

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• Non-small-cell lung cancer

Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT’s capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral “prompt engineering” process, leveraging OpenAI’s API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making.

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Introduction.

Large Language Models (LLMs) 1 , 2 , 3 , 4 , 5 , 6 , such as Generative Pre-trained Transformer (GPT) models represented by ChatGPT, are being utilized for diverse applications across various sectors. In the healthcare industry, early applications of LLMs are being used to facilitate patient-clinician communication 7 , 8 . To date, few studies have examined the potential of LLMs in reading and interpreting clinical notes, turning unstructured texts into structured, analyzable data.

Traditionally, the automated extraction of structured data elements from medical notes has relied on medical natural language processing (NLP) using rule-based or machine-learning approaches or a combination of both 9 , 10 . Machine learning methods 11 , 12 , 13 , 14 , particularly deep learning, typically employ neural networks and the first generation of transformer-based large language models (e.g., BERT). Medical domain knowledge needs to be integrated into model designs to enhance performance. However, a significant obstacle to developing these traditional medical NLP algorithms is the limited existence of human-annotated datasets and the costs associated with new human annotation 15 . Despite meticulous ground-truth labeling, the relatively small corpus sizes often result in models with poor generalizability or make evaluations of generalizability impossible. For decades, conventional artificial intelligence (AI) systems (symbolic and neural networks) have suffered from a lack of general knowledge and commonsense reasoning. LLMs, like GPT, offer a promising alternative, potentially using commonsense reasoning and broad general knowledge to facilitate language processing.

ChatGPT is the application interface of the GPT model family. This study explores an approach to using ChatGPT to extract structured data elements from unstructured clinical notes. In this study, we selected lung cancer pathology reports as the corpus for extracting detailed diagnosis information for lung cancer. To accomplish this, we developed and improved a prompt engineering process. We then evaluated the effectiveness of this method by comparing the ChatGPT output with expert-curated structured data and used case studies to provide insights into how ChatGPT read and interpreted notes and why it made mistakes in some cases.

Data and endpoints

The primary objective of this study was to develop an algorithm and assess the capabilities of ChatGPT in processing and interpreting a large volume of free-text clinical notes. To evaluate this, we utilized unstructured lung cancer pathology notes, which provide diagnostic information essential for developing treatment plans and play vital roles in clinical and translational research. We accessed a total of 1026 lung cancer pathology reports from two web portals: the Cancer Digital Slide Archive (CDSA data) ( https://cancer.digitalslidearchive.org/ ) and The Cancer Genome Atlas (TCGA data) ( https://cBioPortal.org ). These platforms serve as public data repositories for de-identified patient information, facilitating cancer research. The CDSA dataset was utilized as the “training” data for prompt development, while the TCGA dataset, after removing the overlapping cases with CDSA, served as the test data for evaluating the ChatGPT model performance.

From all the downloaded 99 pathology reports from CDSA for the training data, we excluded 21 invalid reports due to near-empty content, poor scanning quality, or missing report forms. Seventy-eight valid pathology reports were included as the training data to optimize the prompt. To evaluate the model performance, 1024 pathology reports were downloaded from cBioPortal. Among them, 97 overlapped with the training data and were excluded from the evaluation. We further excluded 153 invalid reports due to near-empty content, poor scanning quality, or missing report forms. The invalid reports were preserved to evaluate ChatGPT’s handling of irregular inputs separately, and were not included in the testing data for accuracy performance assessment. As a result, 774 valid pathology reports were included as the testing data for performance evaluation. These valid reports still contain typos, missing words, random characters, incomplete contents, and other quality issues challenging human reading. The corresponding numbers of reports used at each step of the process are detailed in Fig. 1 .

Exclusions are accounted for due to reasons such as empty reports, poor scanning quality, and other factors, including reports of stage IV or unknown conditions.

The specific task of this study was to identify tumor staging and histology types which are important for clinical care and research from pathology reports. The TNM staging system 16 , outlining the primary tumor features (T), regional lymph node involvement (N), and distant metastases (M), is commonly used to define the disease extent, assign prognosis, and guide lung cancer treatment. The American Joint Committee on Cancer (AJCC) has periodically released various editions 16 of TNM classification/staging for lung cancers based on recommendations from extensive database analyses. Following the AJCC guideline, individual pathologic T, N, and M stage components can be summarized into an overall pathologic staging score of Stage I, II, III, or IV. For this project, we instructed ChatGPT to use the AJCC 7 th edition Cancer Staging Manual 17 as the reference for staging lung cancer cases. As the lung cancer cases in our dataset are predominantly non-metastatic, the pathologic metastasis (pM) stage was not extracted. The data elements we chose to extract and evaluate for this study are pathologic primary tumor (pT) and pathologic lymph node (pN) stage components, overall pathologic tumor stage, and histology type.

Overall Performance

Using the training data in the CDSA dataset ( n  = 78), we experimented and improved prompts iteratively, and the final prompt is presented in Fig. 2 . The overall performance of the ChatGPT (gpt-3.5-turbo-16k model) is evaluated in the TCGA dataset ( n  = 774), and the results are summarized in Table 1 . The accuracy of primary tumor features (pT), regional lymph node involvement (pN), overall tumor stage, and histological diagnosis are 0.87, 0.91, 0.76, and 0.99, respectively. The average accuracy of all attributes is 0.89. The coverage rates for pT, pN, overall stage and histological diagnosis are 0.97, 0.94, 0.94 and 0.96, respectively. Further details of the accuracy evaluation, F1, Kappa, recall, and precision for each attribute are summarized as confusion matrices in Fig. 3 .

Final prompt for information extraction and estimation from pathology reports.

For meaningful evaluation, the cases with uncertain values, such as “Not Available”, “Not Specified”, “Cannot be determined”, “Unknown”, et al. in reference and prediction have been removed. a Primary tumor features (pT), b regional lymph node involvement (pN), c overall tumor stage, and d histological diagnosis.

Inference and Interpretation

To understand how ChatGPT reads and makes inferences from pathology reports, we demonstrated a case study using a typical pathology report in this cohort (TCGA-98-A53A) in Fig. 4a . The left panel shows part of the original pathology report, and the right panel shows the ChatGPT output with estimated pT, pN, overall stage, and histology diagnosis. For each estimate, ChatGPT gives the confidence level and the corresponding evidence it used for the estimation. In this case, ChatGPT correctly extracted information related to tumor size, tumor features, lymph node involvement, and histology information and used the AJCC staging guidelines to estimate tumor stage correctly. In addition, the confidence level, evidence interpretation, and case summary align well with the report and pathologists’ evaluations. For example, the evidence for the pT category was described as “The pathology report states that the tumor is > 3 cm and < 5 cm in greatest dimension, surrounded by lung or visceral pleura.” The evidence for tumor stage was described as “Based on the estimated pT category (T2a) and pN category (N0), the tumor stage is determined to be Stage IB according to AJCC7 criteria.” It shows that ChatGPT extracted relevant information from the note and correctly inferred the pT category based on the AJCC guideline (Supplementary Fig. 1 ) and the extracted information.

a TCGA-98-A53A. An example of a scanned pathological report (left panel) and ChatGPT output and interpretation (right panel). All estimations and support evidence are consistent with the pathologist’s evaluations. b The GPT model correctly inferred pT as T2a based on the tumor’s size and involvement according to AJCC guidelines.

In another more complex case, TCGA-50-6590 (Fig. 4b ), ChatGPT correctly inferred pT as T2a based on both the tumor’s size and location according to AJCC guidelines. Case TCGA-44-2656 demonstrates a more challenging scenario (Supplementary Fig. 2 ), where the report only contains some factual data without specifying pT, pN, and tumor stage. However, ChatGPT was able to infer the correct classifications based on the reported facts and provide proper supporting evidence.

Error analysis

To understand the types and potential reasons for misclassifications, we performed a detailed error analysis by looking into individual attributes and cases where ChatGPT made mistakes, the results of which are summarized below.

Primary tumor feature (pT) classification

In total, 768 cases with valid reports and reference values in the testing data were used to evaluate the classification performance of pT. Among them, 15 cases were reported with unknown or empty output by ChatGPT, making the coverage rate 0.97. For the remaining 753 cases, 12.6% of pT was misclassified. Among these misclassification cases, the majority were T1 misclassified as T2 (67 out of 753 or 8.9%) or T3 misclassified as T2 (12 out of 753, or 1.6%).

In most cases, ChatGPT extracted the correct tumor size information but used an incorrect rule to distinguish pT categories. For example, in the case TCGA-22-4609 (Fig. 5a ), ChatGPT stated, “Based on the tumor size of 2.0 cm, it falls within the range of T2 category according to AJCC 7th edition for lung carcinoma staging manual.” However, according to the AJCC 7 th edition staging guidelines for lung cancer, if the tumor is more than 2 cm but less than 3 cm in greatest dimension and does not invade nearby structures, pT should be classified as T1b. Therefore, ChatGPT correctly extracted the maximum tumor dimension of 2 cm but incorrectly interpreted this as meeting the criteria for classification as T2. Similarly, for case TCGA-85-A4JB, ChatGPT incorrectly claimed, “Based on the tumor size of 10 cm, the estimated pT category is T2 according to AJCC 7th edition for lung carcinoma staging manual.” According to the AJCC 7 th edition staging guidelines, a tumor more than 7 cm in greatest dimension should be classified as T3.

a TCGA-22-4609 illustrates a typical case where the GPT model uses a false rule, which is incorrect by AJCC guideline. b Case TCGA-39-5028 shows a complex case where there exist two tumors and the GPT model only capture one of them. c Case TCGA-39-5016 reveals a case where the GPT model made a mistake for getting confused with domain terminology.

Another challenging situation arose when multiple tumor nodules were identified within the lung. In the case of TCGA-39-5028 (Fig. 5b ), two separate tumor nodules were identified: one in the right upper lobe measuring 2.1 cm in greatest dimension and one in the right lower lobe measuring 6.6 cm in greatest dimension. According to the AJCC 7 th edition guidelines, the presence of separate tumor nodules in a different ipsilateral lobe results in a classification of T4. However, ChatGPT classified this case as T2a, stating, “The pathology report states the tumor’s greatest diameter as 2.1 cm”. This classification would be appropriated if the right upper lobe nodule were a single isolated tumor. However, ChatGPT failed to consider the presence of the second, larger nodule in the right lower lobe when determining the pT classification.

Regional lymph node involvement (pN)

The classification performance of pN was evaluated using 753 cases with valid reports and reference values in the testing data. Among them, 27 cases were reported with unknown or empty output by ChatGPT, making the coverage rate 0.94. For the remaining 726 cases, 8.5% of pN was misclassified. Most of these misclassification cases were N1 misclassified as N2 (32 cases). The AJCC 7th edition staging guidelines use the anatomic locations of positive lymph nodes to determine N1 vs. N2. However, most of the misclassification cases were caused by ChatGPT interpreting the number of positive nodes rather than the locations of the positive nodes. One such example is the case TCGA-85-6798. The report states, “Lymph nodes: 2/16 positive for metastasis (Hilar 2/16)”. Positive hilar lymph nodes correspond to N1 classification according to AJCC 7th edition guidelines. However, ChatGPT misclassifies this case as N2, stating, “The pathology report states that 2 out of 16 lymph nodes are positive for metastasis. Based on this information, the pN category can be estimated as N2 according to AJCC 7th edition for lung carcinoma staging manual.” This interpretation is incorrect, as the number of positive lymph nodes is not part of the criteria used to determine pN status according to AJCC 7th edition guidelines. The model misinterpreted pN2 predictions in 22 cases due to similar false assertions.

In some cases, the ChatGPT model made classification mistakes by misunderstanding the locations’ terminology. Figure 5c shows a case (TCGA-39-5016) where the ChatGPT model recognized that “6/9 peribronchial lymph nodes involved, “ corresponding with classification as N1, but ChatGPT misclassified this case as N2. By AJCC 7th edition guidelines, N2 is defined as “Metastasis in ipsilateral mediastinal and/or subcarinal lymph node(s)”. The ChatGPT model did not fully understand that terminology and made misclassifications.

Pathology tumor stage

The overall tumor stage classification performance was evaluated using 744 cases with valid reports and reference values as stage I, II and III in the testing data. Among them, 18 cases were reported as unknown or empty output by ChatGPT making the coverage rate as 0.94. For the remaining 726 cases, 23.6% of the overall stage was misclassified. Since the overall stage depends on individual pT and pN stages, the mistakes could come from misclassification of pT or pN (error propagation) or applying incorrect inference rules to determine the overall stage from pT and pN (incorrect rules). Looking into the 56 cases where ChatGPT misclassified stage II as stage III, 22 cases were due to error propagation, and 34 were due to incorrect rules. Figure 6a shows an example of error propagation (TCGA-MP-A4TK). ChatGPT misclassified the pT stage from T2a to T3, and then this mistake led to the incorrect classification of stage IIA to stage IIIA. Figure 6b illustrates a case (TCGA-49-4505) where ChatGPT made correct estimation of pT and pN but made false prediction about tumor stage by using a false rule. Among the 34 cases affected by incorrect rules, ChatGPT mistakenly inferred tumor stage as stage III for 26 cases where pT is T3 and pN is N0, respectively. For example, for case TCGA-55-7994, ChatGPT provided the evidence as “Based on the estimated pT category (T3) and pN category (N0), the tumor stage is determined to be Stage IIIA according to AJCC7 criteria”. According to AJCC7, tumors with T3 and N0 should be classified as stage IIB. Similarly, error analysis for other tumor stages shows that misclassifications come from both error propagation and applying false rules.

a Case TCGA-MP-A4TK: An example of typical errors GPT made in the experiments, i.e. GPT took false rule and further led to faulty propagation. b Case TCGA-49-4505: The GPT model made false estimation of Stage IIIA with a false rule, although it made correct inference with T2b and N1.

Histological diagnosis

The classification performance of histology diagnosis was evaluated using 762 cases with valid reports and reference values in the testing data. Among them, 17 cases were reported as either unknown or empty output by ChatGPT, making the coverage rate 0.96. For the remaining 745 cases, 6 ( < 1%) of histology types were misclassified. Among the mistakes that ChatGPT made for histology diagnosis, ChatGPT misclassified 3 of them as “other” type and 3 cases of actual “other” type (neither adenocarcinomas nor squamous cell carcinomas) as 2 adenocarcinomas and 1 squamous cell carcinoma. In TCGA-22-5485, two tumors exist: one squamous cell carcinoma and another adenocarcinoma, which should be classified as the ‘other’ type. However, ChatGPT only identified and extracted information for one tumor. In the case TCGA-33-AASB, which is the “other” type of histology, ChatGPT captured the key information and gave it as evidence: “The pathology report states the histologic diagnosis as infiltrating poorly differentiated non-small cell carcinoma with both squamous and glandular features”. However, it mistakenly estimated this case as “adenocarcinoma”. In another case (TCGA-86-8668) of adenocarcinoma, ChatGPT again captured key information and stated as evidence, “The pathology report states the histologic diagnosis as Bronchiolo-alveolar carcinoma, mucinous” but could not tell it is a subtype of adenocarcinoma. Both cases reveal that ChatGPT still has limitations in the specific domain knowledge in lung cancer pathology and the capability of correcting understanding its terminology.

Analyzing irregularities

The initial model evaluation and prompt-response review uncovered irregular scenarios: the original pathology reports may be blank, poorly scanned, or simply missing report forms. We reviewed how ChatGPT responded to these anomalies. First, when a report was blank, the prompt contained only the instruction part. ChatGPT failed to recognize this situation in most cases and inappropriately generated a fabricated case. Our experiments showed that, with the temperature set at 0 for blank reports, ChatGPT converged to a consistent, hallucinated response. Second, for nearly blank reports with a few random characters and poorly scanned reports, ChatGPT consistently converged to the same response with increased variance as noise increased. In some cases, ChatGPT responded appropriately to all required attributes but with unknown values for missing information. Last, among the 15 missing report forms in a small dataset, ChatGPT responded “unknown” as expected in only 5 cases, with the remaining 10 still converging to the hallucinated response.

Reproducibility evaluation

Since ChatGPT models (even with the same version) evolve over time, it is important to evaluate the stability and reproducibility of ChatGPT. For this purpose, we conducted experiments with the same model (“gpt-3.5-turbo-0301”), the same data, prompt, and settings (e.g., temperature = 0) twice in early April and the middle of May of 2023. The rate of equivalence between ChatGPT estimations in April and May on key attributes of interest (pT, pN, tumor stage, and histological diagnosis) is 0.913. The mean absolute error between certainty degrees in the two experiments is 0.051. Considering the evolutionary nature of ChatGPT models, we regard an output difference to a certain extent as reasonable and the overall ChatGPT 3.5 model as stable.

Comparison with other NLP methods

In order to have a clear perspective on how ChatGPT’s performance stands relative to established methods, we conducted a comparative analysis of the results generated by ChatGPT with two established methods: a keyword search algorithm and a deep learning-based Named Entity Recognition (NER) method.

Data selection and annotation

Since the keyword search and NER methods do not support zero-shot learning and require human annotations on the entity level, we carefully annotated our dataset for these traditional NLP methods. We used the same training and testing datasets as in the prompt engineering for ChatGPT. The training dataset underwent meticulous annotation by experienced medical professionals, adhering to the AJCC7 standards. This annotation process involved identifying and highlighting all relevant entities and text spans related to stage, histology, pN, and pT attributes. The detailed annotation process for the 78 cases required a few weeks of full-time work from medical professionals.

Keyword search algorithm using wordpiece tokenizer

For the keyword search algorithm, we employed the WordPiece tokenizer to segment words into subwords. We compiled an annotated entity dictionary from the training dataset. To assess the performance of this method, we calculated span similarities between the extracted spans in the validation and testing datasets and the entries in the dictionary.

Named Entity Recognition (NER) classification algorithm

For the NER classification algorithm, we designed a multi-label span classification model. This model utilized the pre-trained Bio_ClinicalBERT as its backbone. To adapt it for multi-label classification, we introduced an additional linear layer. The model underwent fine-tuning for 1000 epochs using the stochastic gradient descent (SGD) optimizer. The model exhibiting the highest overall F1 score on the validation dataset was selected as the final model for further evaluation in the testing dataset.

Performance evaluation

We evaluated the performance of both the keyword search and NER methods on the testing dataset. We summarized the predicted entities/spans and their corresponding labels. In cases where multiple related entities were identified for a specific category, we selected the most severe entities as the final prediction. Moreover, we inferred the stage information for corpora lacking explicit staging information by aggregating details from pN, pT, and diagnosis, aligning with the AJCC7 protocol. The overall predictions for stage, diagnosis, pN, and pT were compared against the ground truth table to gauge the accuracy and effectiveness of our methods. The results (Supplementary Table S1 ) show that the ChatGPT outperforms WordPiece tokenizer and NER Classifier. The average accuracy for ChatGPT, WordPiece tokenizer, and NER Classifier are 0.89, 0.51, and 0.76, respectively.

Prompt engineering process and results

Prompt design is a heuristic search process with many elements to consider, thus having a significantly large design space. We conducted many experiments to explore better prompts. Here, we share a few typical prompts and the performance of these prompts in the training data set to demonstrate our prompt engineering process.

Output format

The most straightforward prompt without special design would be: “read the pathology report and answer what are pT, pN, tumor stage, and histological diagnosis”. However, this simple prompt would make ChatGPT produce unstructured answers varying in format, terminology, and granularity across the large number of pathology reports. For example, ChatGPT may output pT as “T2” or “pT2NOMx”, and it outputs histological diagnosis as “Multifocal invasive moderately differentiated non-keratinizing squamous cell carcinoma”. The free-text answers will require a significant human workload to clean and process the output from ChatGPT. To solve this problem, we used a multiple choice answer format to force ChatGPT to pick standardized values for some attributes. For example, for pT, ChatGPT could only provide the following outputs: “T0, Tis, T1, T1a, T1b, T2, T2a, T2b, T3, T4, TX, Unknown”. For the histologic diagnosis, ChatGPT could provide output in one of these categories: Lung Adenocarcinoma, Lung Squamous Cell Carcinoma, Other, Unknown. In addition, we added the instruction, “Please make sure to output the whole set of answers together as a single JSON file, and don’t output anything beyond the required JSON file,” to emphasize the requirement for the output format. These requests in the prompt make the downstream analysis of ChatGPT output much more efficient. In order to know the certainty degree of ChatGPT’s estimate and the evidence, we asked ChatGPT to provide the following 4 outputs for each attribute/variable: extracted value as stated in the pathology report, estimated value based on AJCC 7th edition for lung carcinoma staging manual, the certainty degree of the estimation, and the supporting evidence for the estimation. The classification accuracy of this prompt with multiple choice output format (prompt v1) in our training data could achieve 0.854.

Evidence-based inference

One of the major concerns for LLM is that the results from the model are not supported by any evidence, especially when there is not enough information for specific questions. In order to reduce this problem, we emphasize the use of evidence for inference in the prompt by adding this instruction to ChatGPT: “Please ensure to make valid inferences for attribute estimation based on evidence. If there is no available evidence provided to make an estimation, please answer the value as “Unknown.” In addition, we asked ChatGPT to “Include “comment” as the last key of the JSON file.” After adding these two instructions (prompt v2), the performance of the classification in the training data increased to 0.865.

Chain of thought prompting by asking intermediate questions

Although tumor size is not a primary interest for diagnosis and clinical research, it plays a critical role in classifying the pT stage. We hypothesize that if ChatGPT pays closer attention to tumor size, it will have better classification performance. Therefore, we added an instruction in the prompt (prompt v3) to ask ChatGPT to estimate: “tumor size max_dimension: [<the greatest dimension of tumor in Centimeters (cm)>, ‘Unknown’]” as one of the attributes. After this modification, the performance of the classification in the training data increased to 0.90.

Providing examples

Providing examples is an effective way for humans to learn, and it should have similar effects for ChatGPT. We provided a specific example to infer the overall stage based on pT and pN by adding this instruction: “Please estimate the tumor stage category based on your estimated pT category and pN category and use AJCC7 criteria. For example, if pT is estimated as T2a and pN as N0, without information showing distant metastasis, then by AJCC7 criteria, the tumor stage is “Stage IB”.” After this modification (prompt v4), the performance of the classification in the training data increased to 0.936.

Although we can further refine and improve prompts, we decided to use prompt v4 as the final model and apply it to the testing data and get the final classification accuracy of 0.89 in the testing data.

ChatGPT-4 performance

LLM evolves rapidly and OpenAI just released the newest GPT-4 Turbo model (GPT-4-1106-preview) in November 2023. To compare this new model with GPT-3.5-Turbo, we applied this newest GPT model GPT-4-1106 to analyze all the lung cancer pathology notes in the testing data. The classification result and the comparison with the GPT-3.5-Turbo-16k are summarized in Supplementary Table 1 . The results show that GPT-4-turbo performs better in almost every aspect; overall, the GPT-4-turbo model increases performance by over 5%. However, GPT-4-Turbo is much more expensive than GPT-3.5-Turbo. The performance of GPT-3.5-Turbo-16k is still comparable and acceptable. As such, this study mainly focuses on assessing GPT-3.5-Turbo-16k, but highlights the fast development and promise of using LLM to extract structured data from clinical notes.

Analyzing osteosarcoma data

To demonstrate the broader application of this method beyond lung cancer, we collected and analyzed clinical notes from pediatric osteosarcoma patients. Osteosarcoma, the most common type of bone cancer in children and adolescents, has seen no substantial improvement in patient outcomes for the past few decades 18 . Histology grades and margin status are among the most important prognostic factors for osteosarcoma. We collected pathology reports from 191 osteosarcoma cases (approved by UTSW IRB #STU 012018-061). Out of these, 148 cases had histology grade information, and 81 had margin status information; these cases were used to evaluate the performance of the GPT-3.5-Turbo-16K model and our prompt engineering strategy. Final diagnoses on grade and margin were manually reviewed and curated by human experts, and these diagnoses were used to assess ChatGPT’s performance. All notes were de-identified prior to analysis. We applied the same prompt engineering strategy to extract grade and margin information from these osteosarcoma pathology reports. This analysis was conducted on our institution’s private Azure OpenAI platform, using the GPT-3.5-Turbo-16K model (version 0613), the same model used for lung cancer cases. ChatGPT accurately classified both grades (with a 98.6% accuracy rate) and margin status (100% accuracy), as shown in Supplementary Fig. 3 . In addition, Supplementary Fig. 4 details a specific case, illustrating how ChatGPT identifies grades and margin status from osteosarcoma pathology reports.

Since ChatGPT’s release in November 2022, it has spurred many potential innovative applications in healthcare 19 , 20 , 21 , 22 , 23 . To our knowledge, this is among the first reports of an end-to-end data science workflow for prompt engineering, using, and rigorously evaluating ChatGPT in its capacity of batch-processing information extraction tasks on large-scale clinical report data.

The main obstacle to developing traditional medical NLP algorithms is the limited availability of annotated data and the costs for new human annotations. To overcome these hurdles, particularly in integrating problem-specific information and domain knowledge with LLMs’ task-agnostic general knowledge, Augmented Language Models (ALMs) 24 , which incorporate reasoning and external tools for interaction with the environment, are emerging. Research shows that in-context learning (most influentially, few-shot prompting) can complement LLMs with task-specific knowledge to perform downstream tasks effectively 24 , 25 . In-context learning is an approach of training through instruction or light tutorial with a few examples (so called few-shot prompting; well instruction without any example is called 0-shot prompting) rather than fine-tuning or computing-intensive training, which adjusts model weights. This approach has become a dominant method for using LLMs in real-world problem-solving 24 , 25 , 26 . The advent of ALMs promises to revolutionize almost every aspect of human society, including the medical and healthcare domains, altering how we live, work, and communicate. Our study shows the feasibility of using ChatGPT to extract data from free text without extensive task-specific human annotation and model training.

In medical data extraction, our study has demonstrated the advantages of adopting ChatGPT over traditional methods in terms of cost-effectiveness and efficiency. Traditional approaches often require labor-intensive annotation processes that may take weeks and months from medical professionals, while ChatGPT models can be fine-tuned for data extraction within days, significantly reducing the time investment required for implementation. Moreover, our economic analysis revealed the cost savings associated with using ChatGPT, with processing over 900 pathology reports incurring a minimal monetary cost (less than $10 using GPT 3.5 Turbo and less than $30 using GPT-4 Turbo). This finding underscores the potential benefits of incorporating ChatGPT into medical data extraction workflows, not only for its time efficiency but also for its cost-effectiveness, making it a compelling option for medical institutions and researchers seeking to streamline their data extraction processes without compromising accuracy or quality.

A critical requirement for effectively utilizing an LLM is crafting a high-quality “prompt” to instruct the LLM, which has led to the emergence of an important methodology referred to as “prompt engineering.” Two fundamental principles guide this process: firstly, the provision of appropriate context, and secondly, delivering clear instructions about subtasks and the requirements for the desired response and how it should be presented. For a single query for one-time use, the user can experiment with and revise the prompt within the conversation session until a satisfactory answer is obtained. However, prompt design can become more complex when handling repetitive tasks over many input data files using the OpenAI API. In these instances, a prompt must be designed according to a given data feed while maintaining the generality and coverage for various input data features. In this study, we found that providing clear guidance on the output format, emphasizing evidence-based inference, providing chain of thought prompting by asking for tumor size information, and providing specific examples are critical in improving the efficiency and accuracy of extracting structured data from the free-text pathology reports. The approach employed in this study effectively leverages the OpenAI API for batch queries of ChatGPT services across a large set of tasks with similar input data structures, including but not limited to pathology reports and EHR.

Our evaluation results show that the ChatGPT (gpt-3.5-turbo-16k) achieved an overall average accuracy of 89% in extracting and estimating lung cancer staging information and histology subtypes compared to pathologist-curated data. This performance is very promising because some scanned pathology reports included in this study contained random characters, missing parts, typos, varied formats, and divergent information sections. ChatGPT also outperformed traditional NLP methods. Our case analysis shows that most misclassifications were due to a lack of knowledge of detailed pathology terminology or very specialized information in the current versions of ChatGPT models, which could be avoided with future model training or fine-tuning with more domain-specific knowledge.

While our experiments reveal ChatGPT’s strengths, they also underscore its limitations and potential risks, the most significant being the occasional “hallucination” phenomenon 27 , 28 , where the generated content is not faithful to the provided source content. For example, the responses to blank or near-blank reports reflect this issue, though these instances can be detected and corrected due to convergence towards an “attractor”.

The phenomenon of ‘hallucination’ in LLMs presents a significant challenge in the field. It is important to consider several key factors to effectively address the challenges and risks associated with ChatGPT’s application in medicine. Since the output of an LLM depends on both the model and the prompt, mitigating hallucination can be achieved through improvements in GPT models and prompting strategies. From a model perspective, model architecture, robust training, and fine-tuning on a diverse and comprehensive medical dataset, emphasizing accurate labeling and classification, can reduce misclassifications. Additionally, enhancing LLMs’ comprehension of medical terminology and guidelines by incorporating feedback from healthcare professionals during training and through Reinforcement Learning from Human Feedback (RLHF) can further diminish hallucinations. Regarding prompt engineering strategies, a crucial method is to prompt the GPT model with a ‘chain of thought’ and request an explanation with the evidence used in the reasoning. Further improvements could include explicitly requesting evidence from input data (e.g., the pathology report) and inference rules (e.g., AJCC rules). Prompting GPT models to respond with ‘Unknown’ when information is insufficient for making assertions, providing relevant context in the prompt, or using ‘embedding’ of relevant text to narrow down the semantic subspace can also be effective. Harnessing hallucination is an ongoing challenge in AI research, with various methods being explored 5 , 27 . For example, a recent study proposed “SelfCheckGPT” approach to fact-check black-box models 29 . Developing real-time error detection mechanisms is crucial for enhancing the reliability and trustworthiness of AI models. More research is needed to evaluate the extent, impacts, and potential solutions of using LLMs in clinical research and care.

When considering using ChatGPT and similar LLMs in healthcare, it’s important to thoughtfully consider the privacy implications. The sensitivity of medical data, governed by rigorous regulations like HIPAA, naturally raises concerns when integrating technologies like LLMs. Although it is a less concern to analyze public available de-identified data, like the lung cancer pathology notes used in this study, careful considerations are needed for secured healthcare data. More secured OpenAI services are offered by OpenAI security portal, claimed to be compliant to multiple regulation standards, and Microsoft Azure OpenAI, claimed could be used in a HIPAA-compliant manner. For example, de-identified Osteosarcoma pathology notes were analyzed by Microsoft Azure OpenAI covered by the Business Associate Agreement in this study. In addition, exploring options such as private versions of these APIs, or even developing LLMs within a secure healthcare IT environment, might offer good alternatives. Moreover, implementing strong data anonymization protocols and conducting regular security checks could further protect patient information. As we navigate these advancements, it’s crucial to continuously reassess and adapt appropriate privacy strategies, ensuring that the integration of AI into healthcare is both beneficial and responsible.

Despite these challenges, this study demonstrates our effective methodology in “prompt engineering”. It presents a general framework for using ChatGPT’s API in batch queries to process large volumes of pathology reports for structured information extraction and estimation. The application of ChatGPT in interpreting clinical notes holds substantial promise in transforming how healthcare professionals and patients utilize these crucial documents. By generating concise, accurate, and comprehensible summaries, ChatGPT could significantly enhance the effectiveness and efficiency of extracting structured information from unstructured clinical texts, ultimately leading to more efficient clinical research and improved patient care.

In conclusion, ChatGPT and other LLMs are powerful tools, not just for pathology report processing but also for the broader digital transformation of healthcare documents. These models can catalyze the utilization of the rich historical archives of medical practice, thereby creating robust resources for future research.

Data processing, workflow, and prompt engineering

The lung cancer data we used for this study are publicly accessible via CDSA ( https://cancer.digitalslidearchive.org/ ) and TCGA ( https://cBioPortal.org ), and they are de-identified data. The institutional review board at the University of Texas Southwestern Medical Center has approved this study where patient consent was waived for using retrospective, de-identified electronic health record data.

We aimed to leverage ChatGPT to extract and estimate structured data from these notes. Figure 7a displays our process. First, scanned pathology reports in PDF format were downloaded from TCGA and CDSA databases. Second, R package pdftools, an optical character recognition tool, was employed to convert scanned PDF files into text format. After this conversion, we identified reports with near-empty content, poor scanning quality, or missing report forms, and those cases were excluded from the study. Third, the OpenAI API was used to analyze the text data and extract structured data elements based on specific prompts. In addition, we extracted case identifiers and metadata items from the TCGA metadata file, which was used to evaluate the model performance.

a Illustration of the use of OpenAI API for batch queries of ChatGPT service, applied to a substantial volume of clinical notes — pathology reports in our study. b A general framework for integrating ChatGPT into real-world applications.

In this study, we implemented a problem-solving framework rooted in data science workflow and systems engineering principles, as depicted in Fig. 7b . An important step is the spiral approach 30 to ‘prompt engineering’, which involves experimenting with subtasks, different phrasings, contexts, format specifications, and example outputs to improve the quality and relevance of the model’s responses. It was an iterative process to achieve the desired results. For the prompt engineering, we first define the objective: to extract information on TNM staging and histology type as structured attributes from the unstructured pathology reports. Second, we assigned specific tasks to ChatGPT, including estimating the targeted attributes, evaluating certainty levels, identifying key evidence of each attribute estimation, and generating a summary as output. The output was compiled into a JSON file. In this process, clinicians were actively formulating questions and evaluating the results.

Our study used the “gpt-3.5-turbo” model, accessible via the OpenAI API. The model incorporates 175 billion parameters and was trained on various public and authorized documents, demonstrating specific Artificial General Intelligence (AGI) capabilities 5 . Each of our queries sent to ChatGPT service is a “text completion” 31 , which can be implemented as a single round chat completion. All LLMs have limited context windows, constraining the input length of a query. Therefore, lengthy pathology reports combined with the prompt and ChatGPT’s response might exceed this limit. We used OpenAI’s “tiktoken” Python library to estimate the token count to ensure compliance. This constraint has been largely relaxed by the newly released GPT models with much larger context windows. We illustrate the pseudocode for batch ChatGPT queries on a large pathology report set in Supplementary Fig. 5 .

Model evaluation

We evaluated the performance of ChatGPT by comparing its output with expert-curated data elements provided in the TCGA structured data using the testing data set. Some staging records in the TCGA structured data needed to be updated; our physicians curated and updated those records. To mimic a real-world setting, we processed all reports regardless of data quality to collect model responses. For performance evaluation, we only used valid reports providing meaningful text and excluded the reports with near-empty content, poor scanning quality, and missing report forms, which were reported as irregular cases. We assessed the classification accuracy, F1, Kappa, recall, and precision for each attribute of interest, including pT, pN, overall stage, and histology types, and presented results as accuracy and confusion matrices. Missing data were excluded from the accuracy evaluation, and the coverage rate was reported for predicted values as ‘unknown’ or empty output.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The lung cancer dataset we used for this study is “Pan-Lung Cancer (TCGA, Nat Genet2016)”, ( https://www.cbioportal.org/study/summary?id=nsclc_tcga_broad_2016 ) and the “luad” and “lusc” subsets from CDSA ( https://cancer.digitalslidearchive.org/ ). We have provided a reference regarding how to access the data 32 . We utilized the provided APIs to retrieve clinical information and pathology reports for the LUAD (lung adenocarcinoma) and LUSC (lung squamous cell carcinoma) cohorts. The pediatric data are the EHR data from UTSW clinic services. The data is available from the corresponding author upon reasonable request and IRB approval.

Code availability

All codes used in this paper were developed using APIs from OpenAI. The prompt for the API is available in Fig. 2 . Method-specific code is available from the corresponding author upon request.

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Acknowledgements

This work was partially supported by the National Institutes of Health [P50CA70907, R35GM136375, R01GM140012, R01GM141519, R01DE030656, U01CA249245, and U01AI169298], and the Cancer Prevention and Research Institute of Texas [RP230330 and RP180805].

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Jingwei Huang, Donghan M. Yang, Ruichen Rong, Kuroush Nezafati, Colin Treager, Shidan Wang, Xian Cheng, Yujia Guo, Guanghua Xiao, Xiaowei Zhan & Yang Xie

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Contributions

J.H., Y.X., X.Z. and G.X. designed the study. X.Z., K.N., C.T. and J.H. prepared, labeled, and curated lung cancer datasets. D.M.Y., X.C., Y.G., L.J.K. prepared, labeled, and curated osteosarcoma datasets. Z.C. provided critical inputs as pathologists. Y.X., G.X., E.P. provided critical inputs for the study. J.H. implemented experiments with ChatGPT. R.R. and K.N. implemented experiments with N.L.P. J.H., Y.X., G.X. and S.W. conducted data analysis. Y.X., G.X., J.H., X.Z., D.M.Y. and R.R. wrote the manuscript. All co-authors read and commented on the manuscript.

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Correspondence to Xiaowei Zhan or Yang Xie .

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Huang, J., Yang, D.M., Rong, R. et al. A critical assessment of using ChatGPT for extracting structured data from clinical notes. npj Digit. Med. 7 , 106 (2024). https://doi.org/10.1038/s41746-024-01079-8

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