Flipkart Data Engineer Interview Questions & Practice

Flipkart Data Engineering interviews are SQL-heavy with strong Spark and data-modeling rounds — commerce-scale problems like order dedup, rolling GMV metrics and funnel analytics. Practice matching question styles below.

What to emphasise for Flipkart

Flipkart works on large-scale Indian e-commerce, so it is worth going in strong on high-traffic order and catalogue pipelines, slowly changing dimensions, and handling seasonal spikes such as sale events where volumes jump by an order of magnitude. 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

• Latest Order Per Customer (sql, easy, +50 XP)
• Customers With at Least One Order (sql, easy, +50 XP)
• Customers Who Have Never Placed an Order (sql, easy, +50 XP)
• Total Amount Spent Per Customer (With Zero) (sql, easy, +50 XP)
• E-commerce: Customers Who Placed an Order (IN Subquery) (sql, easy, +50 XP)
• E-commerce: Count Orders Per Customer (sql, easy, +50 XP)
• E-commerce: Customers With No Orders (NOT IN) (sql, easy, +50 XP)
• Retail: Total Amount Spent Per Customer (sql, easy, +50 XP)
• ROW_NUMBER: Assign Unique Rank to Each Employee (sql, easy, +50 XP)
• RANK: Rank Employees by Salary (With Gaps) (sql, easy, +50 XP)
• SUM OVER: Calculate Running Total of Daily Sales (sql, easy, +50 XP)
• LEAD: Show Next Day's Sales Amount (sql, easy, +50 XP)
• ROW_NUMBER: Remove Duplicate Rows and Keep One Per Order (sql, easy, +50 XP)
• Orders: List Orders With Customer Names (INNER JOIN) (sql, easy, +50 XP)
• Customers Who Never Placed an Order (sql, easy, +50 XP)
• Find Average Order Amount for Each Customer (sql, easy, +50 XP)
• Find Customers Who Placed More Than 5 Orders (HAVING) (sql, easy, +50 XP)
• Find the Most Recent Order Date for Each Customer (sql, easy, +50 XP)
• Find Customers Who Spent More Than ₹1000 in Total (HAVING SUM) (sql, easy, +50 XP)
• Find the Customer Who Placed the Most Orders (sql, easy, +50 XP)

What is asked in Flipkart Data Engineer interviews?

Hands-on SQL (window functions, rolling metrics), Spark transformations, data modeling for commerce events, and pipeline troubleshooting scenarios.

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