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The Data Engineering Interview Study Guide
For your FAANG and other technical interviews
Interviewing for any technical position generally requires preparing, studying, and long, all-day interviews.
Data engineering interviews, like other technical interviews, require plenty of preparation. There are a number of subjects that need to be covered in order to ensure you are ready for back-to-back questions.
Some positions require Hadoop, others SQL. Some roles require understanding statistics, while still others require heavy amounts of system design.
We have gathered many of the resources that we have used to study and get jobs at companies in the FAANG family as well as other major tech companies. We have yet to find one that requires you to know anything about Hadoop during the interview, so that has not been included in this study guide.
We recommend asking the recruiter if you aren’t sure which type of interview you will be facing. Some companies are very good at keeping interviews consistent, but even then, teams can deviate depending on what they are looking for. Here are some examples of what we have noticed about some companies' data engineering interviews.
Amazon — SQL- and database-design heavy as well as general ETL design. Surprisingly, no Python.
Netflix —SQL- and code-heavy, with the expectation that you can not only write SQL and code but can optimize them.
“They asked about SQL queries to find time difference between two events given certain condition. ” — Netflix data engineer on Glassdoor
Expedia — Big Data questions, like what is Spark and RDDs, as well as SQL and Python.
Due to this variance, we’ve created a checklist to keep track of what subject areas you have already studied and what you still need to cover: data engineering study checklist.
Also, I recently created a video guide to walk through the data engineering interview study guide.
Let’s get started with SQL.
