Razorpay Data Analyst interview typically runs 2 rounds: OA, technical interview. The process usually moves quickly, and candidates reported poor follow-up after interviews.
$950K
Avg. Base Comp
$1110K
Avg. Total Comp
2-3
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Razorpay is less interested in polished storytelling than in whether you can reason cleanly through messy data problems. A recurring theme is SQL that tests logic, not memorization: indexing choices, cumulative sums, self-joins, execution order, and medium-difficulty table questions all show up early and often. Even when the interviewer is supportive, as one candidate noted, they still seem to be checking whether you can stay precise under pressure and explain why a query works, not just produce the answer.
What makes Razorpay distinctive is the way it layers product thinking on top of technical fundamentals. Multiple candidates said the deeper discussion moved into RCA, case studies, and how they would tailor analysis for a Product Manager versus leadership. That tells us the bar is not just “can you query the data,” but can you turn the query into a decision-ready narrative. Basic Python and A/B testing appear as supporting signals, but they do not seem to outweigh the need for strong analytical judgment.
We also see a pattern in the candidate experience itself: the interviews felt fair and often collaborative, but the follow-through was inconsistent. That means candidates should prepare for a process where the live conversation may feel encouraging, yet the real separator is whether your case reasoning feels specific and commercially grounded. At Razorpay, the strongest signal is a candidate who can move from SQL output to business implication without hand-waving.
Synthetized from 2 candidates 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.
Featured question at Razorpay
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 | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Top Three Salaries | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Monthly Customer Report | |
| Experiment Validity | |
| First Touch Attribution | |
| First to Six | |
| Last Transaction | |
| Compute Deviation | |
| Bank Fraud Model | |
| Download Facts | |
| Top 3 Users | |
| Button AB Test | |
| 500 Cards | |
| Random SQL Sample | |
| Minimum Change | |
| Subscription Overlap | |
| Month Over Month | |
| Prime to N | |
| Paired Products | |
| Upsell Transactions |
Synthesized from candidate reports. Individual experiences may vary.
The process can start with a Hackerearth assessment worth 90 marks. It mixes CAT-level aptitude with probability and combinatorics, plus two SQL questions that test practical query logic rather than just syntax.
This round is heavily SQL-focused and begins with a brief introduction to your current role and past experience. Expect medium-difficulty SQL on tables, indexing decisions, cumulative sum and self-join problems, execution order of SQL commands, and some basic Python and A/B testing questions.
The next round goes deeper into product analytics, root cause analysis, and case-based problem solving. Candidates may also face a puzzle and be asked to explain how they would present insights differently to a Product Manager versus leadership.
After the technical rounds, HR communicates the final decision. In the reported experiences, this step was inconsistent and candidates were sometimes left without a response.