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.
$85K
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.
I applied to Swiggy for a Data Analyst role and was invited to take an online assessment on HackerEarth. The test was non-proctored and lasted about 70 minutes, with 8 questions across varying difficulty levels. Most of the weight fell on SQL — I'd say 5 or 6 questions were purely SQL, ranging from easy to advanced, alongside a couple of case-style problems that required me to extract business insights from realistic datasets. The SQL questions tested window functions, nested subqueries, CTEs, and joins pretty heavily. One question involved finding customers whose city with the most orders matched their city with the latest order from the previous month — the kind of problem that requires careful logic and multiple joins. Another asked me to analyze funnel drop-off and identify top-performing restaurants. There was also a basic math question on salary aggregation that felt straightforward compared to the others. The datasets were realistic, with food orders, delivery, and restaurant information, so it felt grounded in Swiggy's actual business. I could skip questions and come back to them, which was helpful when I got stuck. Honestly, 70 minutes felt tight. The SQL questions weren't algorithm-heavy in a LeetCode sense, but they demanded precision and domain understanding. My experience ended at the online assessment, and I didn't receive an offer. Looking back, the main challenge wasn't complexity for its own sake, but rather combining multiple SQL concepts under time pressure while interpreting ambiguous business requirements from the problem statements.
Prep tip from this candidate
Focus on window functions and CTEs applied to realistic business datasets like order funnels and customer segmentation — Swiggy's assessment heavily favors SQL problems that extract actionable insights rather than simple aggregation. Practice writing queries that combine joins, subqueries, and window functions in a single problem, and allocate extra time since 70 minutes is tight for the depth and breadth they test.
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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 | |
|---|---|
| Forecasting New Year Revenue | |
| Z and t-Tests | |
| Increased Cancellations | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Experiment Validity | |
| Monthly Customer Report | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Button AB Test | |
| First to Six | |
| Compute Deviation | |
| Month Over Month | |
| Download Facts | |
| 500 Cards | |
| Network Experiment Design | |
| Random SQL Sample | |
| Longest Streak Users | |
| Subscription Overlap | |
| Group Success | |
| Prime to N | |
| Average Order Value | |
| Upsell Transactions | |
| Paired Products | |
| Top 3 Users | |
| Swipe Precision |
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.
