Razorpay Data Engineer Interview Questions & Practice

Razorpay Data Engineering interviews test practical SQL on payments data (settlements, dedup, reconciliation-style problems), Python, and warehouse modeling. The challenges below mirror those styles.

What to emphasise for Razorpay

Razorpay works on payments and financial infrastructure, so it is worth going in strong on transactional data correctness, auditability, reconciliation logic and secure handling of sensitive data, alongside the usual SQL and pipeline design. Treat that as where to spend your last few days of revision, not as a script — the fundamentals below are what actually get tested, and a candidate who can write clean SQL and explain a pipeline end to end does well regardless of the company. Note that interview formats change frequently and vary by team and level, so use this as preparation guidance rather than a guarantee of what you will be asked.

What a Data Engineer interview usually covers

Most Data Engineering loops test four things, in some combination. First, SQL — almost always the deepest-tested skill, with joins, aggregation, window functions and top-N-per-group questions appearing again and again. Second, data modelling and pipeline design: how you would ingest a source, where you would clean it, how you would handle late or duplicate records, and how the tables would be laid out for consumption. Third, coding in Python or PySpark, usually a transformation rather than a puzzle. Fourth, a discussion of something you have actually built, where the interviewer probes your decisions and what you would change.

How to practise so it sticks

Reading solutions creates a false sense of readiness. Write the query yourself, run it, and check the output against the expected result — that feedback loop is what turns recognition into recall. Work in patterns rather than one-off puzzles: once you can write a top-N-per-group query from memory, a whole family of questions becomes routine. Then practise explaining your approach out loud before you type, because most interviews assess your reasoning as much as your final answer. Finish by rehearsing two or three projects in enough detail to discuss the trade-offs you made.

A four-week preparation plan

Week one: rebuild SQL fundamentals — joins, grouping, subqueries and CTEs — until they are automatic. Week two: window functions and the analytics patterns built on them (running totals, ranking, period-over-period, deduplication). Week three: PySpark — the DataFrame API, joins and shuffles, partitioning, and why a job is slow. Week four: system-style design questions and behavioural preparation, plus timed mixed practice so you are used to switching between question types. Throughout, keep a short list of the mistakes you actually make and re-test yourself on those, since that is where the marginal gains are.

Practice challenges

• Banking: Customer Account Type and Balance (sql, easy, +50 XP)
• Running Total Revenue (sql, medium, +100 XP)
• Banking: Total Amount Deposited Per Customer (sql, medium, +100 XP)
• Identifying Users with Purchase Frequency Spikes (sql, hard, +200 XP)

How do I prepare for a Razorpay Data Engineer interview?

Drill SQL window functions and dedup patterns on transaction-like data, brush up Python parsing problems, and be ready to design an ingestion-to-warehouse pipeline end to end.

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