Airbnb Data Engineer interview typically runs 7–8 rounds: recruiter screen, technical phone screen, then a final loop covering coding, SQL, data modeling, system design, behavioral, and culture/values. The process spans several weeks and distinctively tests software-engineering-quality coding alongside data architecture and ETL expertise.
$160K
Avg. Base Comp
$457K
Avg. Total Comp
5-6
Typical Rounds
3-5 weeks
Process Length
Our candidates report that Airbnb's data engineer process is genuinely two-layered in a way that catches people off guard. The take-home is SQL-heavy and sets an immediate technical bar, but what follows in the full loop is much broader — covering software-engineering-quality coding, data modeling, distributed systems, and a dedicated values conversation. The hidden challenge is that each layer demands a different mode of thinking, and candidates who prepare only for one tend to stall in the other.
A recurring theme across candidate experiences is that the modeling and system design discussions are where Airbnb separates strong engineers from great ones. The questions — trial user segmentation, payment pipelines, booking aggregations — are all grounded in real marketplace data problems. Interviewers aren't just checking whether your schema is correct; they're listening for how you reason about data flow, event reconciliation, and reliability at scale in a two-sided marketplace context. Candidates who connect their design choices back to the business shape of the data consistently report more productive conversations than those who stay at the schema level.
What makes this process non-obvious is the values and culture component sitting alongside the technical rounds. Airbnb takes its mission seriously, and we've seen candidates underestimate how much the behavioral and leadership discussions weigh in the final decision. The live coding rounds are unforgiving on precision, but the overall signal Airbnb seems to be optimizing for is an engineer who can move fluidly between implementation and architecture and articulate decisions in a way that fits a globally distributed, trust-driven platform.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Airbnb process.
The first thing I had to do was a take-home HackerRank, and that set the tone pretty quickly. It had 3 to 4 SQL questions and 2 Python coding questions, so it was less about chatting through my background and more about whether I could just get through the work cleanly and efficiently. After that, I moved into a virtual onsite with five rounds. The mix was pretty broad: one round on DSA-style coding, one more general coding round, one data modeling round, one behavioral round, and one end-to-end system design round. It felt like they were trying to cover both hands-on implementation and the bigger picture of how I think about data systems.
The hardest parts for me were the coding rounds because they were live and there wasn’t much room to wander. The data modeling and system design rounds were also important, but they were more about explaining tradeoffs and structure than grinding through syntax. I appreciated that the process was fairly well-rounded, but it was definitely a lot to prepare for because each round tested something different. I didn’t get an offer in the end, so my main takeaway is to be ready for a SQL-heavy take-home and then a very broad onsite that includes both coding and architecture, not just one or the other.
Prep tip from this candidate
Drill timed SQL take-home style problems with 3 to 4 queries plus a couple of Python tasks, then practice explaining a data model and an end-to-end system design out loud. Don’t focus only on DSA, because the onsite also included a separate general coding round and behavioral round.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
| Question | |
|---|---|
| Trial User Segmentation | |
| Uber User Journey | |
| Listing Bookings Aggregation | |
| Causal Email Journey | |
| Data Pipelines and Aggregation | |
| String Palindromes | |
| Approval Drop | |
| Payment Data Pipeline | |
| Statistically Significant Test | |
| Experiment Validity | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Download Facts | |
| Random SQL Sample | |
| Over-Budget Projects | |
| Permutation Palindrome | |
| Month Over Month | |
| Google Maps Improvement | |
| The Brackets Problem | |
| Find the First Non-Repeating Character in a String | |
| Hurdles In Data Projects | |
| Average Order Value | |
| Top 3 Users | |
| User Experience Percentage | |
| Third Purchase | |
| Maximum Profit | |
| Rectangle Overlap |
Synthesized from candidate reports. Individual experiences may vary.
An initial conversation with a recruiter to discuss your background, interest in Airbnb, and the role. This stage also covers logistics, compensation expectations, and timeline before moving to technical evaluation.
Candidates complete a take-home HackerRank with 3 to 4 SQL questions and 2 Python coding questions. The focus is on solving problems cleanly and efficiently, with SQL being a particularly heavy component.
A live technical screen focused on coding and SQL problem-solving. This round assesses whether candidates can work through data engineering problems in real time before advancing to the full onsite loop.
Two separate live coding rounds covering DSA-style problems and general coding. These rounds are fast-paced with little room to wander, testing software-engineering-quality implementation skills under time pressure.
One round focused on data modeling and ETL design, and one end-to-end system design round covering distributed systems and data architecture. Both rounds emphasize explaining tradeoffs and structural thinking rather than syntax.
A dedicated behavioral round assessing alignment with Airbnb's culture and values, including leadership and cross-functional collaboration. Candidates should be prepared to discuss past experiences with concrete examples.