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 question that stuck with me was to build a dashboard using an AI agentic flow. That's a different kind of prompt from the usual BI exercise, and it meant thinking about how an agent would gather data, make decisions, and feed results into a visual output, not just how to lay out charts. I had prepared for standard dashboarding and SQL, so this one took some adjusting.
The process itself was hard to follow. After agreeing on dates and times with the recruiter, I kept receiving invitations with different schedules, and corrections came slowly. This happened across several rounds. Some interviews were postponed shortly before they were due to start, usually citing panel unavailability or an interviewer on sick leave. A few interviewers joined very late, and some didn't join at all. Some rounds seemed to have no clear purpose, and several questions didn't connect to the role.
I cleared all the earlier technical rounds, but my final technical round was rescheduled three times before it was cancelled. I was then told the hiring process had been put on hold for all positions. I didn't get an offer. The lack of communication was the most frustrating part of the whole process.
Prep tip from this candidate
Be ready to design a dashboard around an AI agentic workflow, covering how the agent gathers, decides, and presents data. Also confirm every interview slot in writing with the recruiter, since invitations here frequently conflicted.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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| 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 | |
| First to Six | |
| Monthly Customer Report | |
| Download Facts | |
| First Touch Attribution | |
| Last Transaction | |
| Random SQL Sample | |
| Top 3 Users | |
| Compute Deviation | |
| Button AB Test | |
| Employee Salaries (ETL Error) | |
| Minimum Change | |
| Lowest Paid | |
| 500 Cards | |
| Raining in Seattle | |
| Bank Fraud Model | |
| Subscription Overlap | |
| Month Over Month |
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.
