Swiggy Data Analyst Interview Guide

This guide is framed as a Data and Business Analytics interview because the available evidence sits in the broader analytics family rather than a cleanly separate Data Analyst lane.

Our candidates report that Swiggy is looking for analysts who can move comfortably between a business question and a working query. The strongest signal in this process is practical SQL fluency: not just writing joins from scratch, but reading an existing query, spotting what it’s doing, and changing it without breaking the logic. That shows up repeatedly in the experiences we’ve seen, along with live screen-shared questions where speed mattered, but only if the reasoning stayed clean. The average-time-utilization prompt is a good example of the bar here — they want someone who can translate an operational problem into a metric, not someone who only knows syntax.

A recurring theme is that Swiggy treats SQL as the core skill and everything else as support. Python questions were described as basic pandas/numpy tasks like deduping or merging without creating NaNs, and statistics appeared in a lightweight, applied way rather than as a deep theory test. We’ve also seen a simple DSA-style two-pointer problem surface, which suggests they care about whether candidates can stay nimble under pressure, not whether they’re specialized in algorithms. The non-obvious make-or-break factor here is precision under live editing: multiple candidates described medium-to-difficult joins, query modifications, and logic checks where a small mistake could derail an otherwise solid answer.