Given events + sessions tables, compute sessionized KPIs with runtime/cost constraints
Out loud, in order: clarify, build it, check it, explain it.
Practise SQLLoadingone moment…
Data engineering interview · Finance, Payments & Fintech
Expert SQL + advanced Python + Spark internals, plus a signature multi-day "craft demonstration" pipeline you present.
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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
1 reported question. See them below
3 reported questions. See them below
2–3 days, then present
High confidence3 reported questions. See them below
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By round. Questions marked Expected are written from the company's standards.
SQLHigh confidence
PythonHigh confidence
PySpark / SparkHigh confidence
Craft demonstrationHigh confidence
Nobody reported these exact questions at Intuit. 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.
Craft-demo pipeline (Kafka/Spark/Delta/Snowflake) with layered models + data quality
Do first (3)Expert SQL (sessionization, cost-aware)
Spark internals (repartition vs coalesce, broadcast, deploy modes)
Warehousing/modeling
ThenManagerial decision-explanation
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Why candidates were turned down, as reported.
Designing only the happy path (no data quality/monitoring/alerting)
Design is named in 27 of 50 company accounts.Weak craft-demo decisions/communication
Behavioral is named in 7 of 50 company accounts.Shaky Spark internals
Spark is named in 25 of 50 company accounts.08
5 reported. Each opens and closes. Composites are labelled.
The loop went: recruiter call, a phone round with SQL and Python, a technical round, a demo of your own work, then a manager round. Questions covered session-based metrics under cost limits, masking personal data so it can be reversed, and PySpark basics: repartition against coalesce, broadcast joins and deploy modes. The demo asked for a batch and streaming design with Kafka, Spark, Delta and Snowflake.
What to take from it Be ready to demo and defend one design you built.
Four rounds with 15 coding problems across SQL, data warehousing, Spark and Python. One round covered warehousing ideas and SQL.
What to take from it Prepare a wide range, with many short problems.
A one-hour screen on data warehousing, data modeling, slowly changing dimensions, SQL joins and case statements. Then a take-home style Spark task and a walk through a project.
What to take from it Know slowly changing dimensions and be able to explain a project.
The first round was about big data ideas, AWS services, SQL and Spark. Then a home assignment and final interviews.
What to take from it Know which AWS services fit which job.
Reviews from 2025 and 2026 call the early rounds fairly easy: a basic binary search and a quick SQL coding question. Some people complain that recruiting is slow.
What to take from it Do not skip the easy rounds. Expect a slow process.
Good first-hand signal. The craft demonstration (with data quality + monitoring) is the differentiator.
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Their reported questions, one at a time, against a stopwatch.
Given events + sessions tables, compute sessionized KPIs with runtime/cost constraints
Out loud, in order: clarify, build it, check it, explain it.
Practise SQL10
Pick your level and the weeks you have.
Built from what Intuit 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
Cloud and tools
Pipeline and system design
Read: Data pipeline chaptersDrill: Pipeline practiceSay it aloud: Data pipeline questionsFull plan: Ace the pipeline round
Rehearse the Intuit loop
Run the question deckThen repeat your weakest topic once more.