Complex queries — joins, window functions, performance tuning
Out loud, in order: clarify, build it, check it, explain it.
Practise SQLLoadingone moment…
Data engineering interview · Commerce, Travel & Marketplaces
Data modeling from a business scenario, SQL + Spark internals (jobs/stages/tasks, JSON-with-Spark), and end-to-end ETL.
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And what its data engineers work with.
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Stages in the order candidates meet them.
5 stages. 4 rounds have reported questions, described below.
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The topics it covers and the questions candidates reported.
2 reported questions. See them below
2 reported questions. See them below
design-from-scenario
High confidence2 reported questions. See them below
1 reported question. See them below
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By round. Questions marked Expected are written from the company's standards.
SQLHigh confidence
SparkHigh confidence
Data ModelingHigh confidence
ETLMedium confidence
Nobody reported these exact questions at Flipkart. They are the questions a loop for each of its top topics is known to ask, so prepare for them as likely, not as confirmed.
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Ranked by how much of the evidence points at each topic.
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Do the first group before the second.
Data modeling (design-from-scenario, e.g., tournament/e-commerce)
Do first (3)SQL (joins, window, tuning)
Spark internals (jobs/stages/tasks) + JSON processing
ETL end-to-end; DSA arrays
ThenProject/case presentation
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Why candidates were turned down, as reported.
Weak data modeling (cannot design from a scenario)
Modeling is named in 14 of 50 company accounts.Shallow Spark internals
Spark is named in 25 of 50 company accounts.Weak SQL performance tuning
SQL is named in 37 of 50 company accounts.08
4 reported, 1 composite. Each opens and closes. Composites are labelled.
A four-round data engineering loop with SQL, Spark and modeling.
What to take from it Expect all three areas in one loop.
An online test (SQL, reasoning, statistics), then a technical round (SQL, modeling from a business case, Spark, a project), then a manager round.
What to take from it Practise modeling a business case from scratch.
Four rounds in 2021. First, data modeling and SQL: design a model for an e-commerce company, then three medium to hard SQL queries. Second, machine coding on JSON documents with Spark. Third, a talk on your project from end to end, Spark internals and tuning, the strengths of NoSQL, array problems and an ETL case from ingestion to warehouse.
What to take from it Practise machine coding with Spark on JSON.
A two-hour machine coding task on JSON files, then sessions to explain the code. The next round was problem solving: the Nth highest element in an array, a linked list question and the lowest common ancestor in a tree.
What to take from it Be ready to explain every line of the code you wrote.
Composite walkthroughs. Built from the company's documented loop, not from one person's account.
This is a composite, not one person's account. It is built from the loop we have on record for Flipkart. Questions reported for this round: Complex queries — joins, window functions, performance tuning; Top-N per category, dedup, sessionize.
What to take from it Practise these questions before you reach this round.
Strong first-hand signal; design-from-scenario data modeling is the differentiator.
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Their reported questions, one at a time, against a stopwatch.
Complex queries — joins, window functions, performance tuning
Out loud, in order: clarify, build it, check it, explain it.
Practise SQL10
Pick your level and the weeks you have.
Built from what Flipkart asks, strongest first. For the wider basics at your level, follow the Mid level plan alongside it.
Spark and big data
Read: Spark chaptersDrill: Spark playgroundSay it aloud: Spark questionsFull plan: Ace the Spark round
Data modeling
Read: Data modeling chaptersDrill: Modeling practiceSay it aloud: Data modeling questionsFull plan: Ace the modeling round
Rehearse the Flipkart loop
Run the question deckThen repeat your weakest topic once more.