Airbnb's Data Analyst interview process reported by candidates ranges from a three-stage SQL-focused loop to a six-round analytical and behavioral gauntlet, with SQL, product-metrics, and stakeholder rounds as recurring themes.
$133K
Avg. Base Comp
$215K
Avg. Total Comp
3-6 rounds
Typical Rounds
Not reported
Process Length
Airbnb's Data Analyst interviews can range from a tight three-stage screen to a full six-round loop, based on two detailed candidate reports. One candidate described a six-round process split between four analytical sessions and two behavioral conversations. It started with a hiring-manager call that probed background and experience. That was followed by a medium-difficulty SQL round with an analyst, a product-metrics round (including a question on designing an A/B test) with an analytics manager, a stakeholder conversation, and additional behavioral rounds. A separate candidate faced a shorter format: a HackerRank assessment with SQL questions and one Python question, a recruiter screen, and two technical panels. One panel covered general coding over nested listing data and the other SQL against impressions/actions event tables. Scheduling the three main stages took over a month of back-and-forth. Notably, that candidate was evaluated on SQL with an unstated expectation of Spark-scale execution thinking rather than just correct analytical output. It's worth asking interviewers directly whether they want a working query or a discussion of how it would scale. A third candidate's post-screen feedback took weeks to arrive, yet they still advanced, so a quiet recruiter inbox isn't necessarily a bad sign.
Synthesized from 6 candidate reports by our editorial team.
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Real interview reports from people who went through the Airbnb process.
I had my second round tech screen interview with Airbnb about four weeks ago. The questions themselves were nothing special — average difficulty, nothing that stood out as particularly hard or easy. But what really caught me off guard was the pace of the process afterward. I emailed my recruiter twice asking for feedback. A week in, she replied saying they were still gathering feedback and would reach out soon. I followed up again a few days later and got an out-of-office message for the rest of the week.
The silence was honestly frustrating. I didn't know where I stood or if I should even bother waiting. I started second-guessing whether my performance had been strong enough. But then, surprisingly, I did move forward to the next round. The whole thing took way longer than I expected — the time between my tech screen and hearing back felt like forever. I kept wondering if the delay was a bad sign, but apparently I was still in contention even though it didn't feel that way. The process itself just moves slowly at Airbnb, or at least it did for me.
Prep tip from this candidate
Expect a slower feedback cycle than typical for big tech — don't read silence as a rejection signal. The tech screen is the critical gate; come prepared with solid SQL and metrics analysis questions, and don't assume delays mean you didn't stand out.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Airbnb
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| Uber User Journey | |
| 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 |
Synthesized from candidate reports. Individual experiences may vary.
One candidate began with a HackerRank assessment of four or five SQL questions plus one Python question, followed by a recruiter screen. Scheduling the three main stages after that took more than a month of back-and-forth. In the six-round account, the first interview was with the hiring manager instead, so the opening step may vary.
In the six-round process reported by one candidate, the first interview was with the hiring manager. The hiring manager asked about relevant experience and knowledge and included some technical questions that were not especially deep. This round functions mainly as a fit and background check before the more technical analytical rounds begin.
Candidates report SQL as the central technical filter. One described a medium-level SQL round with an analyst. Another completed a HackerRank assessment (several SQL questions plus a Python problem), then a General Coding panel on nested listing data and a Data Coding panel. The Data Coding panel used impressions/actions event tables and covered seven-day active-user logic, deduplication, and time-spent estimates. In that case, SQL answers were judged against an unstated Spark-scale execution standard. Clarifying whether they want an analytically correct query or a scalable one may help.
In the six-round account, a later round with an analytics manager, likely the head of the department, combined behavioral questions about team fit with possible product-metrics questions. That candidate's product-metrics prompt asked how to design an A/B test tailored to a specific problem the team was solving. They also listed a case on investigating a decline in long-term stays in Europe using metrics, user segmentation, and hypotheses.
The later stages in the six-round format included a stakeholder round on how you work with other stakeholders, followed by two more behavioral interviews. That candidate did not advance past the stakeholder round, so prepare for it as seriously as the technical rounds. Separately, another candidate waited several weeks for feedback after a second-round tech screen and still advanced, so a slow response doesn't necessarily signal rejection.
