Nasdaq Data Engineer Interview Guide

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.