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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. The average salary (in the US) for a data engineer is $113,597, with some earning as much as $164,000 a year, according to Glassdoor. Dice Insights reported in 2019 that data engineering is a top trending job in tech.
Interviews for data engineer jobs 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.
What is a data engineer’s role within a team or company?
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.
When did you face a challenge in dealing with unstructured data and how did you solve it?
Essentially, a data engineer’s main responsibility is to build the 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.
Walk me through a project you worked on from start to finish.
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.
What algorithm(s) did you use on the project?
They want to know how you think through 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.
What tools did you use on the project?
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 back-end, so if you can communicate strong decision-making abilities, then you’ll shine as a candidate who’s confident in their skills.
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’re lacking professional experience.
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.
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).
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.
What is the difference between a data warehouse and an operational database?
For this question, you can answer 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.
Prepare for your data engineer interview
To prepare for your interview, you may find confidence in reviewing everything you’ve learned from previous roles.
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.
