Large event-driven datasets: joins, aggregations, window functions (PARTITION/LEAD/LAG)
Out loud, in order: clarify, build it, check it, explain it.
Practise SQLLoadingone moment…
Data engineering interview · Commerce, Travel & Marketplaces
SQL is decisive (window functions, marketplace metrics, late events), plus trips/payments modeling and real-time pipeline design.
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And what its data engineers work with.
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Stages in the order candidates meet them.
6 stages. 3 rounds have reported questions, described below.
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The topics it covers and the questions candidates reported.
decisive
High confidence3 reported questions. See them below
3 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.
SQL & AnalyticsHigh confidence
Data ModelingHigh confidence
CodingMedium confidence
Nobody reported these exact questions at Uber. 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.
SQL (window functions PARTITION/LEAD/LAG, marketplace metrics, late/dup events) — decisive
Do first (3)Data modeling (trips/payments/events, SCD, batch+streaming)
Real-time pipeline design
Spark concepts
ThenDP problem-solving
Behavioral ownership
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Why candidates were turned down, as reported.
Weak SQL performance is one of the most common reasons for rejection
SQL is named in 37 of 50 company accounts.Shallow data modeling
Modeling is named in 14 of 50 company accounts.Cannot handle late/duplicate events
Weak pipeline reasoning
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0 reported, 5 composite. Each opens and closes. Composites are labelled.
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 based on reported questions: a recruiter call, a screen with SQL, modeling and pipeline reasoning, then an onsite with marketplace SQL and late events, trip and payment modeling, a real-time pipeline design, a behavioral round on ownership and the hiring manager.
What to take from it Use it as a map of the loop.
This is a composite, not one person's account. It is built from the loop we have on record for Uber. decisive. Questions reported for this round: Large event-driven datasets: joins, aggregations, window functions (PARTITION/LEAD/LAG); Compute marketplace metrics; trends over time; Handle duplicate and late-arriving events.
What to take from it Practise these questions before you reach this round.
This is a composite, not one person's account. It is built from the loop we have on record for Uber. Questions reported for this round: Design schemas for trips, users, payments, or events; Normalization trade-offs, SCDs; Support both batch and streaming consumers.
What to take from it Practise these questions before you reach this round.
This is a composite, not one person's account. It is built from the loop we have on record for Uber. Questions reported for this round: Problem-solving incl. DP-pattern; Spark concepts & programming.
What to take from it Practise these questions before you reach this round.
This is a composite, not one person's account. It is built from the loop we have on record for Uber. Candidates go through 6 stages, in this order: Recruiter, then Tech screen (SQL + modeling), then Onsite (4–5), then Behavioral ownership, then Hiring manager, then Offer.
What to take from it Know the order, so that no stage surprises you.
SQL is the most decisive filter; drill window functions and late/duplicate-event handling hardest.
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Their reported questions, one at a time, against a stopwatch.
Large event-driven datasets: joins, aggregations, window functions (PARTITION/LEAD/LAG)
Out loud, in order: clarify, build it, check it, explain it.
Practise SQL10
Pick your level and the weeks you have.
Built from what Uber 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
Pipeline and system design
Read: Data pipeline chaptersDrill: Pipeline practiceSay it aloud: Data pipeline questionsFull plan: Ace the pipeline round
Behavioral and communication
Read: Behavioral storytellingSay it aloud: Behavioral questions
Rehearse the Uber loop
Run the question deckThen repeat your weakest topic once more.