Airbnb Data Analyst candidates report SQL-heavy assessment and technical interviews, plus product-metrics and behavioral conversations. Prepare for event data, experimentation, and clear stakeholder reasoning.
$141K
Avg. Base Comp
$165K
Avg. Total Comp
6 rounds
Typical Rounds
3-5 weeks
Process Length
Airbnb Data Analyst candidates describe an interview process centered on practical analytics rather than a single generic coding screen. One candidate reported a HackerRank assessment with four or five SQL questions and one Python question, followed by a recruiter screen and technical panels. Another reported six interviews in total, including analytical and behavioral conversations. Because the reports differ in structure, treat the sequence as a set of reported paths rather than one fixed loop.
SQL and data reasoning are the clearest recurring themes. Reported prompts include eligibility logic for Superhost status, analyzing a decline in long-term stays in Europe, impression/action event tables, duplicate-event filtering, repeated button clicks, rolling seven-day activity, and estimating time on page while stating assumptions. Practice explaining the query as well as the analytical choices behind joins, definitions, and edge cases.
Product thinking also appears in the evidence. Candidates were asked how to design an A/B test and how to investigate a product trend through metrics, segmentation, and hypotheses. Be ready to connect a metric to a decision, identify plausible confounders, and explain what follow-up analysis would change your recommendation. Behavioral conversations may cover fit, prior experience, and collaboration with managers or stakeholders. One candidate also encountered expectations about Spark-scale execution during a SQL discussion, so it may help to clarify whether the interviewer wants analytical SQL alone or performance and optimization considerations.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Airbnb process.
The biggest surprise in Airbnb’s Data Analyst process was that the SQL discussion was really being judged through a Spark-at-scale lens, although that expectation was never stated clearly. The process itself was slow: it took more than a month and several back-and-forths just to schedule the three main stages. I started with a HackerRank assessment containing four or five SQL questions plus one Python question. They provided a table schema and ran the SQL against test cases; my Python prompt was essentially to implement the final SQL-style question in code.
After a recruiter screen, I had two technical panels: General Coding and Data Coding. Both of mine were with US-based interviewers, so I had to interview at night in IST. In General Coding, I could use Python or Java and was given a dictionary of listings with nested list-like fields such as amenities, ratings, reviews, guest IDs, and host IDs. I worked through tasks like counting listings by cohost, finding the guest with the lowest average rating while handling ties, and sorting listings by rating with most recent review date as the tiebreaker. The interviewer was helpful and gave hints around edge cases.
The Data Coding round was more frustrating. It was rescheduled once because the panel did not show up initially. The interviewer shared impressions and actions schemas with sample rows and asked SQL questions around yesterday’s activity: most distinct pages per visitor, actions by page, pages with repeated BUTTON CLICK actions, seven-day “super users,” deduplicating exact duplicate impressions, and estimating average time spent per page while stating assumptions. I answered from an analytics-SQL perspective, but the interviewer expected Spark execution and scaling considerations—for example, the behavior of CTEs and pre-aggregation—without setting that context. That mismatch made the round feel unclear, and I was rejected afterward.
My advice is to practice SQL on impression/action event tables, especially rolling seven-day activity, deduplication, joins, and duration assumptions. Also explicitly ask whether they want an analytically correct query or an explanation of how it behaves and should be optimized in Spark.
Prep tip from this candidate
Practice the impressions/actions SQL scenarios—seven-day super-user logic, duplicate-event filtering, repeated actions, and page-duration assumptions—and clarify up front whether answers should be evaluated for Spark-scale execution rather than only analytical correctness.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Airbnb
You're getting reports that riders are complaining about the Uber map showing wrong location pickup spots. How would you go about verifying how frequently this is happening?
| Question | |
|---|---|
| Causal Email Journey | |
| Hurdles In Data Projects | |
| Success Measurement | |
| Order Addresses | |
| Listing Bookings Aggregation | |
| Data Pipelines and Aggregation | |
| Model Product Performance Degradation | |
| Ticket Reservation Locking | |
| String Palindromes | |
| Approval Drop | |
| Trial User Segmentation | |
| Deciding Between Solutions | |
| Client Solution Pushback | |
| Payment Data Pipeline | |
| Reward Experiment | |
| Dynamic Demand Pricing | |
| Google Docs Drop | |
| Underpricing Algorithm | |
| Statistically Significant Test | |
| Docs Metrics | |
| Interpreting Fraud Detection Trends | |
| Experiment Validity | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Button AB Test | |
| Monthly Customer Report | |
| User Experience Percentage | |
| 500 Cards |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reported a HackerRank assessment with four or five SQL questions plus one Python question, followed by a recruiter screen. The assessment used a provided schema and test cases; candidates may encounter practical analytics-style coding rather than only conceptual questions.
Candidates report technical panels covering SQL and data coding. Examples include impressions and actions, deduplication, rolling seven-day user logic, page-duration assumptions, and listing data with nested fields. One report says the interviewer also expected discussion of Spark-scale execution and pre-aggregation.
A separate candidate reported product-metric and behavioral discussions with managers and stakeholders. Reported prompts included designing an A/B test and investigating declining long-term stays through metrics, segmentation, and hypotheses; behavioral questions may explore experience and cross-functional fit.
