Company Interview Guide
Airbnb pioneered the modern data culture and created Apache Airflow. Their DE interviews test marketplace data modeling, experimentation infrastructure, and the ability to make data accessible across an entire organization. Here is what each round evaluates and how to prepare.
Three stages from recruiter call to offer decision.
Initial conversation about your experience and interest in Airbnb. The recruiter evaluates your background with data platforms, pipeline orchestration, and experimentation infrastructure. Airbnb has one of the strongest data cultures in tech, so they look for candidates who understand how data drives product decisions and can articulate the role of data engineering in enabling that.
SQL problems set in a marketplace context: bookings, listings, reviews, search interactions. Airbnb phone screens test aggregation, window functions, and multi-step analytical queries. The interviewer evaluates clarity of thought and how you handle ambiguity in problem definitions. You may need to ask clarifying questions about business logic before writing queries.
Five rounds covering SQL deep dive, system design, coding (Python), data modeling, and a core values interview. System design at Airbnb emphasizes experimentation platforms, search ranking data pipelines, and pricing analytics. The core values interview evaluates alignment with Airbnb's mission and how you champion the data culture. Data modeling often involves marketplace schemas that support both operational and analytical workloads.
Real question types from each round. The guidance shows what the interviewer looks for.
Join searches to bookings on user_id where booking_ts BETWEEN search_ts AND search_ts + INTERVAL 24 HOUR. Group by city and month. Discuss attribution: what if a user searched multiple cities before booking?
Calculate avg response time per host per quarter. Use LAG to get prior quarter. Filter where current > 1.5 * prior. Discuss how to handle hosts with very few requests (small sample sizes).
LEFT JOIN listings to bookings, filter WHERE booking_id IS NULL. Join to search_impressions, group by listing, HAVING COUNT >= 100. Discuss what this indicates about listing quality or pricing.
Define check functions for each validation. Return a structured report with pass/fail per check. Discuss how to set dynamic thresholds (e.g., expected row count based on day of week and season) and how to handle check failures in a pipeline.
Event collection, experiment assignment logging, metric computation (pre-experiment and during), statistical analysis pipeline. Discuss metric standardization (Minerva), avoiding p-hacking with pre-registered metrics, and how to handle interaction effects between overlapping experiments.
Batch features (historical booking rate, review scores) from Spark, real-time features (current availability, recent views) from a feature store. Discuss feature freshness requirements, how to backfill features for new listings, and serving latency constraints for search.
Fact: bookings, searches, reviews, messages. Dimensions: listings, hosts, guests, locations. Discuss the two-sided marketplace: host metrics (occupancy rate, response time, revenue) vs guest metrics (search-to-book conversion, repeat booking rate). Define grain carefully for each fact table.
Airbnb's data culture means DEs serve analysts, PMs, and executives. Describe building self-serve tools, documentation, or training. Quantify impact: 'Reduced analyst time-to-answer from 2 days to 30 minutes' or 'Enabled 50 PMs to query experiment results independently.'
What makes Airbnb different from other companies.
Apache Airflow was born at Airbnb. Interviewers expect you to understand DAG-based orchestration, dependency management, idempotent tasks, and retry strategies. If you use Airflow, know it well. If you use a competitor (Dagster, Prefect), be ready to compare.
Airbnb trains all employees on data literacy and has a centralized metrics layer (Minerva). DEs are expected to help the entire organization, not just serve other engineers. Prepare examples of making data accessible to non-technical users.
Every Airbnb metric has a host side and a guest side. When modeling data, always consider both perspectives: a booking is revenue for the host and an experience for the guest. Interviewers notice candidates who naturally think about both sides.
Airbnb runs thousands of experiments concurrently. DE pipelines compute metrics for these experiments. Understand how to design pipelines that support A/B testing: logging experiment assignment, computing treatment vs control metrics, and handling novelty effects.
Airbnb DE questions test marketplace thinking and data culture. Practice with problems that mirror two-sided platform analytics.
Practice Airbnb-Level SQL