14 Data Engineer Interview Questions and How to Answer Them

Interviewing for any job can be stressful. In the technology industry, data engineer jobs can be incredibly competitive. Many people are attracted to these careers because they are in demand, offer high salaries, and have positive long-term job growth.

As you prepare for a future interview, be proud of how far you’ve come on your data engineering journey. Due to the sheer competition, some job searchers report applying for hundreds of jobs in big data until they actually get called for an interview, despite having the qualifications and skills needed, so don’t get discouraged if securing one takes longer than you expected. Once you do, you’ll need to clearly explain why and how you employed certain data methods and algorithms in a previous project in order to land the job.

If you are currently a data scientist or analyst, software engineer, or business intelligence analyst, you may be interviewing for a data engineer role. The average base salary (in the US) for a data engineer is $120,082, with some earning as much as $197,000 a year, according to Glassdoor as of March 2024 [1]. Dice Insights reported in 2019 that data engineering is a top trending job in tech [2]. 

Read more: What Does a Data Engineer Do and How Do I Become One?

Interviews for big data roles tend to be focused on technical, rather than behavioral, questions. Here are general, process, and technical questions you might be asked during your data engineer interview.

Want to build job-relevant data engineer skills today?

Consider enrolling in one of these popular courses on Coursera to build your data engineering skills: In IBM's Data Warehouse Engineer Professional Certificate, you'll learn how to deploy, manage, secure, operationalize, monitor, and optimize relational database systems like MySQL and PostgreSQL.

In Microsoft's Microsoft Azure for Data Engineering course, you'll learn how to use Microsoft Azure for common data engineering tasks.

General data engineer interview questions

Interviewers want to know about you and why you’re interested in becoming a data engineer. Data engineering is a technical role, so while you’re less likely to be asked behavioral questions, these higher-level questions might show up early in your interview.

1. Tell me about yourself.

What they’re really asking: What makes you a good fit for this job? 

This question is asked so often in interviews that it can seem generic and open-ended, but it’s really about your relationship with data engineering. Keep your answer focused on your path to becoming a data engineer. What attracted you to this career or industry? How did you develop your technical skills? 

The interviewer might also ask:

• Why did you choose to pursue a career in data engineering?

• Describe your path to becoming a data engineer.

2. What is a data engineer’s role within a team or company?

What they’re really asking: What is a data engineer responsible for?

For this question, recruiters want to know that you’re aware of the duties of a data engineer. What do they do? What role do they play within a team? You should be able to describe the typical responsibilities, as well as who a data engineer works with on a team. If you have experience as a data scientist or analyst, you may want to describe how you’ve worked with data engineers in the past. 

The interviewer might also ask:

• What do data engineers do?

• How do data engineers work within a team?

• What impact does a data engineer have?

3. When did you face a challenge in dealing with unstructured data, and how did you solve it?

What they’re really asking: How do you deal with problems? What are your strengths and weaknesses?

Essentially, a data engineer’s main responsibility is to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. This question aims to ask about any obstacles you may have faced when dealing with a problem, and how you solved it.

This is your time to shine, where you can describe how you make data more accessible through coding and algorithms. Rather than explaining the technicalities at this point, remember the specific responsibilities listed in the job description and see if you can incorporate them into your answer.

The interviewer might also ask:

• How do you solve a business problem?

• What is your process for dealing with and solving problems during a project?

• Can you describe a time when you encountered a problem and solved it in an innovative manner?

Hear a data professional at Google, Hallie, describe her career and its impact in this lecture from Google's Prepare Data for Exploration course:

Data engineer process questions

Most often, data engineer job candidates will be asked about their projects. If you’ve never been a data engineer previously, you can describe projects that you either worked on for a class or posted on GitHub, a code hosting platform that promotes collaboration among developers.

4. Walk me through a project you worked on from start to finish.

What they’re really asking: How do you think through the process of acquiring, cleaning, and presenting data?

You’ll definitely be asked a question about your thought process and methodology for completing a project. Hiring managers want to know how you transformed the unstructured data into a complete product. You’ll want to practice explaining your logic for choosing certain algorithms in an easy-to-understand manner, to demonstrate you really know what you’re talking about. Afterward, you’ll be asked follow-up questions based on this project.

The interviewer might also ask:

• What was the most challenging project you’ve worked on, and how did you complete it?

• What is your process when you start a new project?

5. What algorithm(s) did you use on the project?

What they’re really asking: Why did you choose this algorithm, and can you compare it with other similar algorithms? 

They want to know what you think about choosing one algorithm over another. It might be easiest to focus on a project that you worked on and link any follow-up questions to that project. If you have an example of a project and algorithm that relates to the company’s work, then choose that one to impress the interviewer. List the models you worked with, and then explain the analysis, results, and impact.

The interviewer might also ask:

• What is the scalability of this algorithm?

• What would you do differently if you were to do the project again?

6. What tools did you use on the project?

What they’re really asking: How did you arrive at your decision to use certain tools?

Data engineers must manage huge swaths of data, so they need to use the right tools and technologies to gather and prepare it all. If you have experience using different tools such as Hadoop, MongoDB, and Kafka, you’ll want to explain which one you used for that particular project.

You can go into detail about the ETL (extract, transform, and load) systems you used to move data from databases into a data warehouse, such as Stitch, Alooma, Xplenty, and Talend. Some tools work better for the back-end, so if you can communicate strong decision-making abilities, then you’ll shine as a candidate who’s confident in their skills.

The interviewer might also ask:

• What are your favorite tools to use, and why?

• Compare and contrast two or three tools that you used on a recent project. 

Data engineer technical questions

Some interviewers might follow up with more technical questions, for which you may want to refresh your memory prior to the interview. Familiarize yourself with the concepts listed in the job description and practice talking through them.

7. What is data modeling?

Data modeling is the initial step toward designing the database and analyzing data. You’ll want to explain that you’re capable of showing the relationship between structures, first with the conceptual model, then the logical model, and followed by the physical model.

Brush up on fundamentals of data modeling in this lecture from Google's The Path to Insights: Data Models and Pipelines course:

8. Explain the difference between structured data and unstructured data.

Data engineers must turn unstructured data into structured data for data analysis using different methods for transformation. First, you can explain the difference between the two.

Structured data is made up of well-defined data types with patterns (using algorithms and coding) that make them easily searchable, whereas unstructured data is a bundle of files in various formats, such as videos, photos, texts, audio, and more.

Unstructured data exists in unmanaged file structures, so engineers collect, manage, and store it in database management systems (DBMS), turning it into structured data that is searchable. Unstructured data might be inputted through manual entry or batch processing with coding, so ELT is the tool used to transform and integrate data into a cloud-based data warehouse. 

Second, you can share a situation in which you transformed data into a structured format, drawing from learning projects if you lack professional experience.

9. What are the design schemas of data modeling?

Design schemas are fundamental to data engineering, so try to be accurate while explaining the concepts in everyday language. There are two schemas: star schema and snowflake schema.

Star schema has a fact table that has several associated dimension tables, so it looks like a star and is the simplest type of data warehouse schema. Snowflake schema is an extension of a star schema and adds additional dimension tables that split the data up, flowing out like a snowflake’s spokes.

10. What are big data’s four Vs?

The four Vs are volume, velocity, variety, and veracity. Chances are, the interviewer will ask you not just what they are, but why they matter. You might explain that big data is about compiling, storing, and exploiting huge amounts of data to be useful for businesses. The four Vs must create a fifth V, which is value. 

• Volume: Refers to the size of the data sets (terabytes or petabytes) that need to be processed—for example, all of the credit card transactions that occur in a day in Latin America. 

• Velocity: Refers to the speed at which the data is generated. Instagram posts have high velocity. 

• Variety: Refers to the many sources and file types of structured and unstructured data. 

• Veracity: Refers to the quality of the data being analyzed. Data engineers need to understand different tools, algorithms, and analytics in order to cultivate meaningful information.

11. Tell me some of the important features of Hadoop.

Hadoop is an open-source software framework for storing data and running applications that provides mass amounts of storage and processing power. Your interviewer is testing whether you understand its significance in data engineering, so you’ll want to explain that it is compatible with multiple types of hardware that make it easy to access.

Hadoop supports rapid processing of data, storing it in the cluster which is independent of the rest of its operations. It allows you to create three replicas for each block with different nodes (collections of computers networked together to compute multiple data sets at the same time). 

12. Which ETL tools have you worked with? What is your favorite, and why?

The interviewer is assessing your understanding of and experience with ETL tools. You’ll want to list the tools that you’ve mastered, explain your process for choosing certain tools for a particular project, and choose one. Explain the properties that you like about the tool to validate your decision.

13. What is the difference between a data warehouse and an operational database?

You can answer this question by explaining that databases using Delete SQL statements, Insert, and Update focus on speed and efficiency, so analyzing data can be more challenging. With data warehouses, the primary focus is on calculations, aggregations, and select statements that make it ideal for data analysis.

The final question: Do you have any questions for us?

Most interviews end with this question, in one form or another. Consider this your chance to end on a high note, because not asking questions reflects poorly—it could demonstrate that you are not interested in the company, the role, or learning more about how you could fit in. Prepare a few questions, and select at least two or three to ask during the interview. Common questions include:

• What is the company culture?

• What does a typical day look like in this job?

• What are the expectations for the first three months in the role, and what are the benchmarks for evaluating success?

• Who will I be working with? 

• Is there any other information I can offer to clear up any doubts about my qualifications?

Prepare for your data engineer interview

To prepare for your interview, you may find confidence in reviewing everything you’ve learned from previous roles and courses you’ve taken. Imagine yourself in the interview, whether it is in person or over Zoom, with the hiring manager asking you technical questions.

• Study and master SQL. Review data pipeline systems and emerging technologies in the Hadoop ecosystem. 

• Design a sample data pipeline. Make sure you understand the objective, and how you factor in data lineage, data duplication, loading data, scaling, testing, and end-user access patterns. 

• Learn and review languages. Look at the job description to understand what the role entails. For backend-oriented systems, you’ll want to know Scala, and for analytics and data science-oriented systems, you’ll want to be well-versed in Python.

• Research potential interview questions. Besides those listed above, you may be able to find interview questions for the company on Glassdoor. It’s worth peeking there as part of your prep, in case someone has kindly made that advice available to the public.

• Talk through your process. This is perhaps the most important tip of all. Knowing how to write code and assemble data is not enough, you must be able to communicate your process and decision-making to the interviewers. Practice by talking through a recent project to a friend who is unfamiliar with big data.

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Become a data engineer with Coursera

Practicing data engineering tools and concepts can help you feel more prepared as you apply for roles and participate in interviews. To prepare for your job search and beyond, try some of these top-rated courses:

• Explore a career as a data engineer with the IBM Data Engineering Professional Certificate. Learn foundational data engineering skills while you complete hands-on labs and projects. You’ll be able to use Python and Linux/UNIX shell scripts to extract, transform, and load data, work with big data engines like Hadoop and Spark, and use business intelligence tools to extract insights.

• Practice using top data engineering tools with Duke University's Python, Bash, and SQL Essentials for Data Engineering Specialization. In each course, you'll complete hands-on labs that you can use in a portfolio.

• Prepare for an industry-recognized certification exam with Google Cloud's Data Engineering, Big Data, and Machine Learning on GCP Specialization. This intermediate-level program provides training in support of the Google Cloud Professional Data Engineer certification.