Razorpay analytics interview reports describe SQL-heavy assessment and technical work, followed in one account by product analytics and RCA case discussion.
$88K
Avg. Base Comp
$128K
Avg. Total Comp
2 rounds
Typical Rounds
Not reported
Process Length
Razorpay Data Analyst preparation should center on practical SQL reasoning and clear analytics communication. One candidate described a two-round process: an initial SQL-and-experience conversation, followed by a more technical discussion using product-style case studies, root-cause analysis, basic Python, and a puzzle. Another candidate reported a HackerEarth assessment with aptitude, probability, combinatorics, and SQL before a technical conversation that moved into SQL fundamentals and prior project impact.
The recurring theme is SQL beyond syntax recall. Prepare to reason through cumulative sums, self joins, joins, window functions, indexing choices, and query execution order. Be ready to explain the logic behind an answer, not simply produce a query. The reports also point to business-facing judgment: candidates were asked to discuss project impact and how they would present the same analysis differently to a Product Manager and to leadership.
For case preparation, practice walking through a product analytics or RCA problem in a structured way: clarify the issue, describe the analysis you would run, and communicate a defensible conclusion. One account suggests that the case discussion carried more weight than solving the puzzle alone. Available reports come from adjacent analytics titles, so the exact Data Analyst sequence may vary.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Razorpay process.
The process moved pretty quickly, which I appreciated at first. I had two rounds total. The first round was a mix of SQL and a conversation about my previous work experience, and the interviewer was actually helpful when I got stuck, nudging me in the right direction instead of just letting me flail. The SQL was around medium difficulty, based on tables, so it wasn’t just syntax recall — I had to think through the logic carefully. The second round felt noticeably more technical and was built around case studies plus a puzzle. That round went deeper into product-style analytics and RCA, and I also got a few basic Python questions mixed in. I answered the puzzle correctly and felt okay about the technical parts overall, but the interviewer seemed to be looking for something more specific in the case discussion.
What stood out most was that the interviewers themselves were nice and experienced, but the process after that was frustrating. I went through all the rounds and then HR just ghosted me, which was disappointing given the reputation of the company. So even though the interviews themselves were fair and the first interviewer was supportive, the communication afterward was poor. If you’re preparing for this role, I’d focus on medium-level SQL on tables, basic Python, and being able to walk through RCA and product case studies clearly under pressure. The puzzle wasn’t the hard part; the deeper case reasoning seemed to matter more.
Prep tip from this candidate
Brush up on medium-difficulty SQL over tables, basic Python, and especially RCA/product case study walkthroughs, since the second round leaned more on that than on the puzzle itself.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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In which case would you use a bagging algorithm versus a boosting algorithm
| Question | |
|---|---|
| Hurdles In Data Projects | |
| Z and t-Tests | |
| Production Rollout Challenges | |
| Reddit-like Notifications | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| 2nd Highest Salary | |
| Top Three Salaries | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Experiment Validity | |
| Monthly Customer Report | |
| First Touch Attribution | |
| First to Six | |
| Last Transaction | |
| Top 3 Users | |
| Compute Deviation | |
| Download Facts | |
| Button AB Test | |
| 500 Cards | |
| Bank Fraud Model | |
| Random SQL Sample | |
| Subscription Overlap | |
| Minimum Change | |
| Month Over Month | |
| Prime to N | |
| Paired Products | |
| Upsell Transactions |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reported a 90-mark HackerEarth assessment combining aptitude, probability, combinatorics, and two SQL questions. Treat this as a possible screening stage rather than a universal requirement.
Candidates report SQL-focused technical questioning on table-based problems, indexing, cumulative sums, self-join output, joins, window functions, and query execution order, alongside discussion of past work and impact.
One candidate reported a more technical round with product-style analytics and root-cause-analysis case studies, basic Python, and a puzzle. Practice explaining findings for both product and leadership audiences.
