Nasdaq Data Engineer candidates report an online assessment covering SQL, Python and PySpark, followed by resume-focused and technical conversations. Here is what two reports describe and how to prepare.
$123K
Avg. Base Comp
$140K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Two candidates who interviewed for Nasdaq Data Engineer roles both began with a timed technical assessment, and both ended with no offer. One described a little over an hour of mixed questions across SQL, Python and PySpark, including at least one LeetCode-style problem. The other described a 2-hour technical assessment before a screening interview.
The SQL portion was the most concrete detail. One candidate had to calculate total annual sales per employee and then find the top seller, which combines aggregation with ranking. The same candidate was later asked how to write a database schema in SQL, so practice defining tables, keys and relationships, not just writing queries.
The interview stages differed between the two reports. One candidate had a conversational round about background and resume projects, then technical database questions. The other met a Senior Data Platform Engineer first, was asked to walk through a past project and explain how it relates to data engineering, and then reported a manager interview and a final interview with the manager's manager. Prepare a clear project story that ties your pipeline, SQL or Spark work to the role.
One candidate also reported slow communication after the first round, so it may help to follow up proactively. With only two reports, treat the details as examples rather than a fixed format.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Nasdaq process.
The process started with an online assessment that gave me a little over an hour to work through a bank of questions. That part felt broad rather than deeply specialized: I was expected to answer general questions across the stack, especially SQL, Python, and PySpark, and there was also at least one LeetCode-style problem mixed in. The SQL portion was the most concrete, including a query where I had to calculate total annual sales for each employee and then identify the employee with the highest total sales. It wasn’t especially tricky algorithmically, but it did require being comfortable moving between aggregation and ranking logic quickly.
After that, the interview itself was more conversational at first. I was asked about my personal and family background, then spent time walking through my resume and projects in detail. The final stage shifted into technical database questions, mostly SQL concepts and writing queries. One question was about how to write a database schema in SQL, so they were checking whether I understood the structure of tables and relationships, not just query syntax. Overall the process felt fairly general and stack-oriented, with a mix of behavioral, resume-based, and technical screening. I didn’t get an offer, so my main takeaway was that it helps to be ready for both broad tech-stack knowledge and practical SQL writing under time pressure, not just coding problems.
Prep tip from this candidate
Be ready for a timed assessment that mixes SQL, Python, PySpark, and at least one LeetCode-style question. For SQL, practice aggregation plus top-performer/ranking queries and be able to explain how you would define a basic database schema in SQL.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Nasdaq
Design a database schema for a blogging platform.
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| String Subsequence | |
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| Confidence Interval Explanation | |
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| Finding the Maximum Number in a List |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report a timed assessment as the first step. One described a little over an hour of broad questions across SQL, Python and PySpark plus a LeetCode-style problem; another described a 2-hour technical assessment. Practice aggregation, ranking queries and general coding under time pressure.
One candidate reports a screening interview after the assessment, and another describes a conversational opening about personal background before a resume walkthrough. Prepare a concise summary of your data engineering work.
Candidates report being asked to walk through projects in detail. One was asked to explain how a past project relates to data engineering. Prepare a project story covering the data involved, your design choices, the tools you used and your own contribution.
One candidate reports a final stage of technical database questions, including SQL concepts, writing queries and how to write a database schema in SQL. Review joins, aggregation, ranking and basic table design with keys and relationships.
One candidate reports a manager interview followed by a final interview with the manager's manager, without detailing the content. The same candidate waited about three weeks without hearing back and received no offer. It may help to follow up with the recruiter yourself.
