Nasdaq Data Engineer interview typically runs 3 rounds: online assessment, interview, final technical database round. Timeline is about a few weeks, and the process is broad and stack-oriented.
$137K
Avg. Base Comp
$166K
Avg. Total Comp
3
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Nasdaq is less interested in a single deep specialty than in whether you can move comfortably across the whole data stack. The experience we saw was broad and practical: SQL showed up alongside Python, PySpark, and even a LeetCode-style problem, but the SQL questions were the clearest signal. One candidate was asked to compute annual sales by employee and then rank the top performer, which tells us they care about clean aggregation logic and fast query translation, not just memorized syntax.
A recurring theme is that Nasdaq also wants to understand how you think about data systems, not only how you query them. The conversation moved from background and resume walkthroughs into database design, including a schema-writing prompt for a blogging platform. That points to a preference for candidates who can explain table structure, relationships, and tradeoffs in plain language. We’ve seen this kind of process reward people who can connect their project experience to real-world data modeling decisions, especially when the interviewer shifts from resume details into technical follow-ups.
The non-obvious part is how general the bar feels while still being exacting. The process may sound conversational, but the technical questions are designed to expose gaps quickly: if you can’t switch from behavioral context to SQL design under pressure, it becomes obvious. Our read is that Nasdaq values candidates who are steady across both business-facing discussion and hands-on implementation, with enough breadth to handle whatever part of the stack the interviewer decides to probe.
Synthesized from 1 candidate report 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.
| Question | |
|---|---|
| Subscription Overlap | |
| Merge Sorted Lists | |
| Prime to N | |
| Find the Missing Number | |
| Hurdles In Data Projects | |
| Rectangle Overlap | |
| Google Maps Improvement | |
| Groups of Anagrams | |
| Radix Addition | |
| Missing Housing Data | |
| Most Repetition | |
| Find Duplicate Numbers in a List | |
| Binary Tree Validation | |
| Target Indices | |
| String Subsequence | |
| Nearest Common Ancestor | |
| Longest Increasing Subsequence | |
| Target Value Search | |
| Messenger Service Design | |
| Data Preparation for Imbalanced Data | |
| Previous NaN Values | |
| Dijkstra implementation | |
| Moving Window | |
| Finding the Maximum Number in a List | |
| Filling Supermarket Bag | |
| Impossibly Iterative Fibonacci | |
| Median O(1) | |
| Assumptions of Linear Regression | |
| Inherited Model Evaluation |
Synthesized from candidate reports. Individual experiences may vary.
The process begins with a broad online assessment covering the stack, especially SQL, Python, and PySpark, along with at least one LeetCode-style coding problem. The SQL section includes practical query-writing tasks such as aggregations and ranking, like calculating annual sales by employee and identifying the top performer.
The next stage is a conversational interview that starts with personal and family background questions, then moves into a detailed walkthrough of the candidate’s resume and projects. This portion appears to assess communication skills, experience depth, and overall fit before moving into more technical evaluation.
The final stage focuses on technical database knowledge, with an emphasis on SQL concepts and writing queries. Candidates may also be asked to explain how to design a database schema in SQL, indicating that the interview tests both query fluency and understanding of table structure and relationships.