Name: Top 10+ Data Engineer Interview Questions and Answers
Uploaded: 2026-03-28T09:22:05.000Z
Duration: 13 min 19 s
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and today we're going to talk about the data engineering interview, example questions and solutions,

Modal verbs - B1

and then go over the 8 different types of questions that might get asked for data engineers.

We analyzed thousands of data engineering interview questions,

and we saw that there's pretty common types between the two.

Number 1: the most common interview question type is usually around behavioral interview questions,

and behavioral interview questions get asked to basically every candidate.

But for data engineers, they're going to be a little bit more specific towards data engineering and your previous projects.

For example, one question would be like, "Tell me about a time that you saw an inefficiency in data processing,

and how did you solve it?" Another question would be like, "How would you work with a product manager to develop a new product?

How would you consider the data pipeline side of it?" For these questions, they're really assessing just how you can work with others cross-functionally and other data engineers on your team.

So a lot of this is just talking about your experience, really going into the details about your data engineering knowledge,

and making sure that you can communicate your projects and your experience well.

Let's look at a question in greater detail.

Let's say that the interviewer asks you to describe one of your data engineering projects that you worked on before.

So with this question and a lot of other behavioral interview questions, the easiest way to use is the STAR format.

STAR stands for Situation, Task, Action, and Result.

So describe the situation, what exactly happened with the data engineering project.

Were you basically creating an events database?

Then provide an overview of the actual task at hand.

What was the problem that you were trying to solve, and then how did you actually solve it?

Then talk about the actions that you took.

What did you actually do as part of that project?

Did you actually design the schema yourself, or did you work with another engineer to do so?

And then lastly, tell the interviewer about the results.

Did you improve the data pipeline and data accuracy by X percent?

Did you improve the data quality and help increase the level of confidence in data to the executives?

So stuff like that is really helpful when describing all your projects.

STAR is pretty basic, and you can read more about it below in the description.

Number 2: a second pretty common question we see is just general basic technical questions around data engineering.

So these questions would be something like, "What ETL tools do you use?

What's your experience with cloud technologies, Azure, Spark, other things like that?" These questions are more so understanding your depth of experience using these tools because a lot of these companies might have specific workflows,

and they like to prioritize candidates that have used these tools before.

So if you have experience in Airflow, then you do have an advantage.

And then they're just kind of testing you really basic kind of requirements on that you knew how to use Airflow and that you actually used it before versus kind of putting it on your resume because you read about it once.

The rule of thumb for these questions is just don't put anything on your resume that you actually haven't directly had any experience with yourself,

and then you could probably answer these questions pretty easily.

The third most common type of question is SQL,

and the SQL probably comes up 95 percent of the time.

It's probably the most common technical interview question that shows up for data engineers.

SQL questions are basically core to any data engineering job.

You need to know the fundamentals for just joins, subqueries, case statements,

and you probably will be asked pretty basic level SQL questions up to really hard SQL questions.

Most SQL questions exist to weed out candidates that just don't have any experience.

And so if you've been a data engineer before, this should be pretty easy.

If you haven't and you're a new grad looking to jump into data engineering, you can practice SQL questions on interview query and get really good at them.

Given a user's table, write a query to return only its duplicate rows.

One is named projects and another maps employees to the projects that they're working on.

Given the employees and departments table, write a query to get the top three highest employee salaries by department.

You're given a table that represents search results from searches on Facebook.

Position column represents each position the search result came in,

and the rating column represents the human rating of the result from 1 to 5, where 5 is high relevance and 1 is lower relevance.

Write a query to get the percentage of search queries where all the ratings from the query results are less than a rating of 3.

Okay, let's dive into one of these questions and go over the solution as well.

Given three tables representing customer transactions and customer attributes, write a query to get the average order value by gender.

So we can see from these tables that we actually have to join all three of them likely because of the fact that we need to get the average order value, which is going to be a function of the product times the actual quantity times the price.

All we have to do is join users to transactions and then join that to products as well, and

then group by the sex of the user and then get the average quantity multiplied by the price.

If you guys are looking for more resources for SQL interview questions, I've done a ton of videos of this in the past,

and you can find them in the description below.

The fourth and fifth most common interview questions get asked for data engineers are on data structures and algorithms.

Now, these are actually bucketed into two groups.

One is more so data structures and algorithms that you could expect on questions like LeetCode.

These are testing your ability with just being able to write coding and basically code as a software engineer.

The second type is more so common to actual data manipulation using Python or Pandas.

And so this is basically taking data and manipulating in some way.

But it's not as algorithm focused as the LeetCode type problems are.

For example, you'd be doing something like using Python lists to iterate over date and time, basically munching different kinds of data types, converting ints to strings and strings back to ints,

and then doing some data munching in Pandas, basically doing some feature selection, maybe merging two datasets, stuff like that.

So example of more of the data munching Python type questions are write a function called findByGrams to take a string and return a list of all the bygrams.

So here you're just doing a little bit of data munching.

You're basically going through a really long string and creating bygrams.

So basically kind of like feature selection that you'd expect in data science,

but also shows up in data engineering as well.

The question that's probably more common to LeetCode would be something like given a list of integers and an integer n, write a function called sumToN that finds all the combinations that sum up the value to n.

So this is a pretty common LeetCode question.

Basically, you return a list of integers that sum up to this n target variable that you also input in.

Very common, data engineers are expected just to be able to code and code well.

So this is going to be a basically common part of any interview that you go to.

And so this actually segues really nicely into our sponsor of today's video, which is Interview Query.

So Interview Query is a startup that I founded that basically helps you with your interview prep for any kind of data engineering interview that you have coming up.

Primarily, we have tons of interview questions, courses, and

challenge assessments so you can see how you rank compared to your peers.

Additionally, we also have an interactive code editor where you can run SQL and Python and basically view your test cases.

And also, you could see solutions from other community members as well.

We're actually giving away one free year of Interview Query premium access to anyone who comments on this video talking about their interview experiences as a data engineer or just any questions they have about the data engineering interview.