Swiggy Data and Business Analytics interview typically runs 5 rounds: online assessment, SQL and visualization, statistics and Python case study, problem-solving, and final managerial discussion. It usually takes about 1-2 weeks and is notably SQL-heavy and structured.
$75K
Avg. Base Comp
$111K
Avg. Total Comp
5
Typical Rounds
2-4 weeks
Process Length
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.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Swiggy process.
The process was pretty SQL-heavy from the start, and that was the main thing I wish I had prepared for more deeply. I first had an online assessment that lasted about an hour and mixed analytics questions with SQL and Excel. One of the questions I remember was about finding the average time utilisation for a fleet of vehicles, so it wasn’t just syntax — it was also about interpreting the data and turning the business problem into a query or metric. After that, the interview moved into rounds that kept circling back to SQL, with one round focused on SQL and visualization, another on statistics, basic Python, and a case study, and then a problem-solving round. The final round was a mix of the earlier rounds plus some managerial discussion.
What stood out most was how much they cared about practical SQL ability. In one interview, I was asked to read an existing query and make changes to it, and in another the questions were more advanced, centered on joins and written live on screen while I screen-shared. Those questions ranged from medium to difficult, so speed mattered, but so did being careful with logic. Python came up too, mostly basic pandas/numpy-style tasks like removing duplicates or merging tables without introducing NaNs, and there was also a simple DSA-style two-pointer question that had to be coded in Python. I also got a straightforward SQL theory question on the difference between WHERE and HAVING. I ended up not getting an offer in my case, but the process itself felt smooth and fairly structured. If I were doing it again, I’d spend most of my prep on advanced SQL joins, query editing, and translating business metrics into clean analytics questions, with just enough Python and statistics to handle the supporting rounds.
Prep tip from this candidate
Drill advanced SQL joins and practice editing an existing query live on screen, since that came up more than once. Also be ready for basic pandas tasks like removing duplicates or merging tables, plus a simple two-pointer problem coded in Python.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Swiggy
Create top_ads with the top 3 ads and return the row counts for inner, left, right, and cross joins with ads
| Question | |
|---|---|
| Z and t-Tests | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Experiment Validity | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Button AB Test | |
| First to Six | |
| Compute Deviation | |
| Download Facts | |
| 500 Cards | |
| Network Experiment Design | |
| Random SQL Sample | |
| Longest Streak Users | |
| Subscription Overlap | |
| Month Over Month | |
| Group Success | |
| Prime to N | |
| Weekly Aggregation | |
| Paired Products | |
| Upsell Transactions | |
| Swipe Precision | |
| Average Order Value | |
| Raining in Seattle | |
| Over-Budget Projects |
Synthesized from candidate reports. Individual experiences may vary.
The process starts with an online assessment that mixes analytics questions with SQL and Excel. Candidates are expected to translate a business problem into a metric or query, such as calculating average time utilisation for a fleet of vehicles.
This round focuses heavily on practical SQL skills, including reading an existing query and modifying it live. Visualization-related questions may also appear, and the SQL difficulty can range from medium to difficult, with an emphasis on joins and careful logic.
Candidates are tested on basic statistics, Python, and a case study. Python questions are typically practical pandas/numpy-style tasks such as removing duplicates or merging tables without introducing NaNs.
This round includes a more general problem-solving exercise, which may include a simple DSA-style question. In the reported experience, a two-pointer problem had to be coded in Python.
The final round combines themes from earlier interviews and adds managerial discussion. It serves as a broader evaluation of technical depth, business thinking, and overall fit.