Airbnb Data Engineer Mock Interview

Tell us about a time you had to deliver a data pipeline improvement that directly impacted customer-facing experiences (e.g., search relevance, availability, pricing, trust & safety). What was the business outcome, how did you define success, and what trade-offs did you make?

Airbnb relies heavily on real-time and near-real-time signals across marketplace operations. Describe how you would design an event-driven pipeline for user actions (views, clicks, bookings) to power a live feature or dashboard. What technologies and data modeling choices would you make to ensure low-latency, correctness, and schema evolution?

We often need to balance scale with reliability. Walk us through a production incident where a data quality issue (late data, duplicates, missing events, or incorrect joins) caused wrong metrics or downstream failures. How did you detect it, isolate the root cause, and prevent recurrence?

Suppose you’re asked to migrate a critical pipeline used by multiple teams at Airbnb from a legacy batch ETL job to a more modern architecture (e.g., streaming or dbt-style transformations). How would you plan the migration to minimize downtime, validate parity, and handle backward compatibility with existing consumers?

Imagine you’re building a dataset to support trust and safety decisions: aggregating signals like host verification, cancellation patterns, and reported issues. How would you handle privacy, data governance, and feature leakage concerns while ensuring the dataset is explainable and usable for both analysts and machine learning teams?