Joins, aggregations, window functions, LAG/LEAD
Out loud, in order: clarify, build it, check it, explain it.
Practise SQLLoadingone moment…
Data engineering interview · Big Tech & Enterprise Software
GCP-native, SQL + data-modeling heavy, with scale-flavored system design (trillion-row optimization, real-time ad-click pipelines).
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And what its data engineers work with.
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Stages in the order candidates meet them.
6 stages. 5 rounds have reported questions, described below.
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The topics it covers and the questions candidates reported.
medium–hard
High confidence3 reported questions. See them below
2 reported questions. See them below
3 reported questions. See them below
3 reported questions. See them below
2 reported questions. See them below
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By round. Questions marked Expected are written from the company's standards.
SQLHigh confidence
CodingMedium confidence
Data ModelingHigh confidence
System DesignHigh confidence
Behavioral (Googleyness)Medium confidence
Nobody reported these exact questions at Google. 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/LAG-LEAD, optimization) + modeling (event logs, metrics, star/SCD)
Do first (3)System design: real-time analytics, batch vs streaming, idempotency, skew
BigQuery/Dataflow mental model
Python DS/algo
ThenGoogleyness behavioral stories
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Why candidates were turned down, as reported.
Weak SQL / modeling at scale
SQL is named in 37 of 50 company accounts.Cannot reason about distributed skew or trillion-row optimization
Spark is named in 25 of 50 company accounts.Pipeline design missing idempotency & late-data handling
Design is named in 27 of 50 company accounts.08
5 reported, 1 composite. Each opens and closes. Composites are labelled.
This candidate had a five-round onsite: data modeling, pipeline design, Python coding, advanced SQL, and a Googleyness and behavioral talk.
What to take from it Each round tests something different, so prepare all five, not only SQL.
One candidate said pipeline design was the main filter. The question was to design a real-time pipeline that tracks ad clicks. The interviewers looked at batch against streaming and at data that arrives late.
What to take from it Be ready to say when you would pick streaming, and how you handle late events.
Another candidate was asked to design a pipeline for clicks from millions of users.
What to take from it Start by asking about volume and delay, then draw the pipeline.
Some questions were about running a data system. One was how to deal with 100 alerts a day. Another was what to do when BigQuery costs keep rising.
What to take from it Have a clear way to rank alerts and to find what drives cost.
The behavioral round asked why the candidate wanted to join Google. Another candidate said finalists met leaders to talk about the role and how they would fit.
What to take from it Know why Google, and why this team.
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 joins the questions that guides report: a recruiter call and a hiring manager call, a technical screen with SQL and coding, then a loop with SQL, data modeling for products like YouTube and Ads, a system design for an ad-click pipeline, and a Googleyness round.
What to take from it Use it as a map of the loop, not as the record of one interview.
Glassdoor/Blind were blocked; specific questions are candidate-reported via aggregators. SQL + modeling + scale-aware design dominate.
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Their reported questions, one at a time, against a stopwatch.
Joins, aggregations, window functions, LAG/LEAD
Out loud, in order: clarify, build it, check it, explain it.
Practise SQL10
Pick your level and the weeks you have.
Built from what Google asks, strongest first. For the wider basics at your level, follow the Mid level plan alongside it.
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
Spark and big data
Read: Spark chaptersDrill: Spark playgroundSay it aloud: Spark questionsFull plan: Ace the Spark round
Python and coding
Read: Python chaptersDrill: Python practiceSay it aloud: Python questionsFull plan: Ace the Python round
Cloud and tools
Rehearse the Google loop
Run the question deckThen repeat your weakest topic once more.