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 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 specific data methods and algorithms in a previous project to land the job.

You may be interviewing for a data engineer role if you are a data scientist, analyst, software engineer, or business intelligence analyst. The median salary in India for a data engineer is ₹887,482, with some earning as much as ₹2 million a year, according to Payscale [1]. 

Interviews for prominent data roles tend to focus on technical rather than behavioural questions. Here are general, process, and technical questions interviewers may ask you during a data engineer interview.

General data engineer interview questions

Interviewers want to know about you and why you want to become a data engineer. Data engineering is a technical role, so while you’re less likely to be asked behavioural questions, these higher-level questions might appear 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 and 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, 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 when you encountered a problem and solved it innovatively?

Data engineer process questions

Interviewers typically ask data engineer job candidates about their projects. Suppose you’ve never been a data engineer previously. In that case, you can describe projects you either worked on for a class or posted on GitHub, a code hosting platform promoting 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?

Interviewers will undoubtedly ask at least one question about your thought process and methodology for completing a project. Hiring managers want to know how you transformed unstructured data into a complete product. You’ll want to practice explaining your logic for choosing particular algorithms in an easy-to-understand manner to demonstrate you really know what you’re talking about. Afterward, interviewers will likely ask 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 the process for starting 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 similar algorithms? 

They want to know what you think about choosing one algorithm over another. It might be easiest to focus on a project you worked on and link any follow-up questions to it. If you have an example of a project and algorithm related to the company’s work, 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?

• If you were to do the project again, what would you do differently?

6. What tools did you use on the project?

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

Data engineers must manage huge swaths of data and 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 detail 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, you’ll shine as a candidate who’s confident in their skills.

The interviewer might also ask:

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

• Compare and contrast two or three tools 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 before the interview. Familiarise yourself with the concepts listed in the job description and practice discussing them.

7. What is data modelling?

Data modelling is the initial step towards designing the database and analysing data. You’ll want to explain that you can show the relationship between structures, first with the conceptual model, then the logical model, followed by the physical model.

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 contains well-defined data types with patterns (using algorithms and coding) that make them easily searchable. In contrast, 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 searchable structured data. Unstructured data might get inputted through manual entry or batch processing with coding, so ELT is the tool to transform and integrate data into a cloud-based data warehouse. 

Second, you can share a situation where you transformed data into a structured format, drawing from learning projects if you need more professional experience.

9. What are the design schemas of data modelling?

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

Star schema has a fact table with 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 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. Big data is about compiling, storing, and exploiting vast 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 of data generation. 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 undergoing analysis. Data engineers need to understand different tools, algorithms, and analytics 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, providing mass 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 the rapid processing of data, storing it in a cluster 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 datasets simultaneously). 

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

The interviewer will assess your understanding of and experience with ETL tools. You’ll want to list the tools you’ve mastered, explain your process for choosing specific 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, making analysing data more challenging. Data warehouses focus primarily on calculations, aggregations, and select statements that make it ideal for data analysis.

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

This is the closing question for interviews of all types, with interviewers asking it 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 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. You’ll want to know Scala for backend-oriented systems; 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 if 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 unfamiliar with big data.

Become a data engineer with Coursera

Interested in this in-demand career? Learn the skills you need to become a data engineer in 15 months or less with the IBM Data Engineering Professional Certificate on Coursera. You can 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.

Mastering the Data Engineering Interview

You’ve hit a significant milestone as a computer scientist and are becoming a capable programmer. If you need more assistance with your job search, check out our resource guide for interview tips and more.