Long queries with multiple CTEs, window functions, conditional aggregations
Out loud, in order: clarify, build it, check it, explain it.
Practise SQLLoadingone moment…
Data engineering interview · Commerce, Travel & Marketplaces
Heavy multi-CTE SQL, interval/overlap coding, marketplace data modeling, and a culture-fit round that is a real dealbreaker.
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And what its data engineers work with.
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Stages in the order candidates meet them.
6 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
3 reported questions. See them below
2 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
Coding / Data StructuresMedium confidence
Data ModelingMedium confidence
Values / BehavioralMedium confidence
Nobody reported these exact questions at Airbnb. 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 (multi-CTE, window functions, conditional aggregation) under time pressure
Do first (3)Interval/overlap + top-k coding
One end-to-end ETL/streaming design; Airbnb values STAR stories
Data modeling (marketplace schema, partitioning, SCD)
ThenSpark internals
Airflow
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Why candidates were turned down, as reported.
Culture-fit rejection despite passing technically
Behavioral is named in 7 of 50 company accounts.Weak SQL under time pressure
SQL is named in 37 of 50 company accounts.Shallow system design at senior leveling
Design is named in 27 of 50 company accounts.Messy interval/coding logic
Python is named in 13 of 50 company accounts.08
4 reported, 1 composite. Each opens and closes. Composites are labelled.
A loop in Bangalore that ended in rejection. The candidate covered data quality checks (duplicates, nulls, broken references), and a later round was about past projects and why they chose each technology.
What to take from it Be able to name your data quality checks and the reason behind your tools.
A take-home HackerRank test with three or four SQL questions and two Python questions. SQL tasks reported: top N with ties, time on page using LEAD(), and users with impressions on seven days in a row.
What to take from it Practise ties in ranking, LEAD and consecutive-day problems.
A modeling task: design a model for running booking totals that also keeps the first and last booking times. The candidate used slowly changing dimensions and the interviewer pushed back hard.
What to take from it Be ready to defend a model, and to change it when pushed.
A design question about loading SQL data into Hive with daily partitions and running an ETL over two tables, which the candidate found odd. One candidate said not knowing Scala did not help.
What to take from it Know Hive partitions, and check if the team uses Scala.
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 follows the senior loop that candidates describe: a recruiter call, a phone round with coding and SQL, then a loop with a booking-interval coding task, a top-5-hosts SQL query, a marketplace schema, a real-time fraud design, values questions and the hiring manager.
What to take from it Use it as a map of the loop.
SQL-heavy; the culture-fit round genuinely rejects technically strong candidates.
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Their reported questions, one at a time, against a stopwatch.
Long queries with multiple CTEs, window functions, conditional aggregations
Out loud, in order: clarify, build it, check it, explain it.
Practise SQL10
Pick your level and the weeks you have.
Built from what Airbnb 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
Behavioral and communication
Read: Behavioral storytellingSay it aloud: Behavioral questions
Rehearse the Airbnb loop
Run the question deckThen repeat your weakest topic once more.