Airbnb Data Engineer Interview Questions: the take-home is the gate
Most round-ups of Airbnb data engineer interview questions jump straight to the onsite and skip the part that eliminates the most people: the take-home. Before you speak to anyone, you get a timed HackerRank set — three or four SQL questions and a Python question in about an hour — and on several reports you needed a near-perfect score to advance. Strong engineers who have written SQL for years get cut here, not because the questions are hard but because the standard is unforgiving and the clock is real.
This guide maps the whole loop in 2026: the take-home gate, the virtual onsite that is the most data modelling-heavy of any data-engineering process, the analytical SQL round scored on its own rubric, and the system-design round built around Airflow and ETL. It covers the question shapes Airbnb favours, the trap of confusing its two different coding rounds, and how to prepare for the parts no solved-query list rehearses.
How the Airbnb DE loop runs (2026)
Airbnb's process is long — often four to five weeks with a gap between stages — and it front-loads a hard filter, then weights the onsite toward modelling.
- Take-home (the gate). A timed HackerRank set of three to four SQL questions and one or two Python questions in about an hour. The bar to advance is high, close to perfect.
- Virtual onsite (~5 rounds). An analytical SQL round on its own rubric; a data-coding round; a separate general-coding (DSA) round; a heavily weighted data-modelling round; and a system-design round with Airflow and ETL, plus business questions threaded through.
Everything is framed around Airbnb's marketplace — bookings, hosts, pricing and search events — so prompts tend to read like real analytics investigations rather than abstract puzzles.
On the analytical SQL round the interviewer rarely wants just the query. "Bookings in this market dropped eight percent week over week — write the SQL you'd run first, and tell me what you're ruling out." The round tests how you investigate, out loud, not whether you can recite a join.
The take-home: SQL you have to nearly ace
The take-home is a SQL gauntlet with a Python chaser. Expect three to four SQL questions and one or two Python questions, and expect to need almost all of them right. The SQL favours "first X" and "per-group metric" shapes — the first booking per guest, the median nightly price per market, origin-destination combinations — which lean on aggregation and analytic functions rather than tricks.
-- "first X" shape: the first booking per guest
SELECT guest_id, MIN(booking_ts) AS first_booking_ts
FROM bookings
GROUP BY guest_id;
-- "per-market metric" shape: median nightly price via an analytic function
SELECT market,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY nightly_price) AS median_price
FROM listings
GROUP BY market;
Because the pass bar is so high, the skill is accuracy under time: read every requirement, handle the null and tie cases, and sanity-check your result against a small example before you submit. One sloppy edge case can be the difference between an onsite and a rejection.
The onsite is data-modelling-heavy
If one thing defines the Airbnb DE onsite, it is data modelling. It is widely described as the most modelling-heavy data-engineering loop you will meet, and the modelling round draws the most follow-ups: defend your grain, justify each dimension and fact table, and explain how the model answers the business question — bookings, host analytics, pricing, search. Treat it as a conversation about trade-offs, not a diagram you hand over.
The onsite also contains a quiet trap: the data-coding round and the general-coding round are different, and candidates get cut for confusing them. The data-coding round wants analytical SQL or Python that answers a data question; the general-coding round wants data-structures-and-algorithms thinking. Answering a DSA prompt "how I'd solve it analytically in SQL" is a reported way to fail, so confirm which round you are in before you choose your approach.
System design, Airflow and business sense
The system-design round is concrete and pipeline-shaped. A reported prompt spec'd a SQL dump landing in Hive, partitioned into daily tables, with the interviewer asking for the ETL query that would run on it — so be ready to talk ingestion, partitioning, idempotent backfills and orchestration with Airflow. Threaded through every round is business sense: Airbnb wants a data engineer who ties a modelling or pipeline choice back to a marketplace outcome, not one who stops at the technically correct answer.
That makes the Airbnb loop a test of things reading cannot build: near-perfect SQL under the take-home clock, a data model you can defend out loud, and an investigation you can narrate. Each is read on the four dimensions a scored round grades — problem solving (the right model and investigation), communication (can you justify the grain and explain the metric), technical execution (clean SQL and a sound pipeline), and time management (did you finish the take-home accurately and scope the onsite). A question dump trains none of them.
Data engineering
Airbnb · Take-home set · 60 min
Run the Airbnb take-home SQL set out loud, then defend the result
How to prepare for the Airbnb DE interview
Prepare for the gate and the model, in that order.
- Ace the take-home. Drill timed SQL to near-perfect accuracy on "first X" and per-group-metric shapes, and rehearse checking edge cases before submitting. Almost-right does not advance.
- Practise data modelling out loud. Take a marketplace scenario — bookings, hosts, pricing — and talk through the grain, the facts and the dimensions, defending each choice as if to an interviewer.
- Keep analytical and DSA practice separate. Know which muscle each onsite round wants so you do not bring an analytical answer to a DSA round.
- Rehearse an investigation. Practise answering "metric X dropped — how do you find out why?" with the queries you would run and what you would rule out.
- Learn an orchestration story. Be able to describe a SQL-to-Hive, daily-partitioned pipeline with Airflow and idempotent backfills.
If you need raw volume of timed SQL to reach take-home accuracy first, PipeCode is a sister platform built for exactly that kind of data-engineering practice; use it to get precise and fast, then come back here to rehearse the modelling and investigation rounds out loud.
Data engineering
Airbnb · Take-home set · 60 min
Rehearse defending a data model under follow-ups, out loud
Frequently asked questions
What is the Airbnb data engineer interview process?
It typically starts with a recruiter screen, then a timed take-home, then a virtual onsite of about five rounds. The take-home is a SQL-heavy HackerRank set you must nearly ace to advance. The onsite includes an analytical SQL round on its own rubric, a data-coding round, a separate general-coding (DSA) round, a heavily weighted data-modelling round, and a system-design round built around Airflow and ETL. The whole process runs long, often four to five weeks. The rounds here are modelled on how that loop runs, not drawn from any leaked question set.
What is on the Airbnb DE take-home?
Reports describe three to four SQL questions and one or two Python questions in about an hour, with a high bar to advance — some candidates say they needed every answer correct. The SQL favours analytical shapes: the first booking per guest, the median nightly price per market, origin-destination combinations. The challenge is accuracy under time, so handling null and tie cases and sanity-checking against a small example matters as much as the core query.
How data-modelling-heavy is the Airbnb onsite?
Very — it is widely described as the most data-modelling-heavy data-engineering loop. The modelling round draws the most follow-ups: you pick a grain, justify your fact and dimension tables, and explain how the model answers a marketplace question about bookings, hosts or pricing. Treat it as a trade-off conversation you defend out loud, not a finished diagram, because the interviewer is scoring your reasoning more than the picture.
What's the difference between the data-coding and general-coding rounds?
They test different muscles. The data-coding round wants analytical SQL or Python that answers a data question; the general-coding round wants data-structures-and-algorithms thinking. Candidates have been rejected for answering a general-coding prompt "how I would solve it analytically in SQL", so confirm which round you are in and switch approach accordingly. Prepare both separately rather than assuming every coding question is an analytics question.
What system-design and ETL topics does Airbnb test?
The system-design round is pipeline-shaped. A reported prompt described a SQL dump landing in Hive, partitioned into daily tables, with the interviewer asking for the ETL that would run on it. Be ready to discuss ingestion, partitioning, idempotent backfills, and orchestration with Airflow — and to tie those choices back to the business outcome they support. Business sense runs through every round, so always connect the pipeline to what it lets the company decide.
How do I practise the Airbnb DE onsite out loud?
A solo take-home cannot rehearse the onsite, where you defend a model and investigate a metric while someone pushes back. On Zynter, the Data engineering track runs the Airbnb take-home set against a real schema, and a voice interviewer follows up on your queries and your model, then gives a debrief on problem solving, communication, technical execution and time management. You get a genuine data-engineer mock interview on demand, and the score is a practice signal, not a hiring decision.
Practise it on Zynter
You can ace the take-home. The offer is decided at the onsite, defending a model out loud.
Run the Airbnb take-home set against a real schema, then talk through your data model and a metric investigation with a voice interviewer that keeps asking "why that grain?" and "what would you rule out first?" — and read the debrief. Zynter.ai is on-demand mock interviews modelled on real company loops, with 150+ rounds across 20 companies (Sep 2026) — about $8–$12 for a full 60-minute mock, and two free credits to start.
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