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Airbnb-Specific Data Patterns Questions

Domain-specific data modeling and analytics patterns used in Airbnb-scale product analytics. Covers data schema design, event and transaction patterns, feature engineering templates for predictive models, cohort and lifecycle analytics, geospatial and temporal data patterns, price and demand forecasting signals, AB testing data patterns, and data quality, governance, and lineage considerations relevant to Airbnb data.

EasyTechnical
76 practiced
Write SQL (Postgres/BigQuery) to sessionize user events into sessions using a 30-minute inactivity gap. Given table events(user_id STRING, occurred_at TIMESTAMP, event_type STRING), produce: session_id, user_id, session_start, session_end, event_count. Use window functions and deterministic session_id creation.
MediumSystem Design
62 practiced
Design an event ingestion pipeline that can handle up to 1M events/sec globally with end-to-end ingestion latency under 5 seconds for analytics. Specify components (client SDK, edge ingestion, message broker, stream processing, schema registry, storage), how you'd handle schema evolution, idempotency, backpressure, and region-aware routing. What trade-offs would you make to control cost and ensure reliability?
EasyTechnical
75 practiced
What is a star schema and why is it commonly used for product analytics at Airbnb? Describe the fact table(s) and at least three dimension tables you would create for bookings analytics. For each table, give sample columns and typical join keys and explain how this model supports analytic queries.
MediumTechnical
86 practiced
Design a feature store architecture for Airbnb demand forecasting that must serve both low-latency online features and batch training features. Describe the schema for features, feature granularity (listing-day, listing-hour), freshness SLAs, batch vs streaming feature computes, TTL/retention, and how to guarantee consistency between offline training features and online serving features.
MediumTechnical
79 practiced
Given listings(listing_id, lat FLOAT, lon FLOAT, city STRING) and bookings(booking_id, listing_id, occurred_at TIMESTAMP), write SQL (Postgres/PostGIS or BigQuery) to compute conversion rate per 1km grid cell within a specified city and return the top-3 hotspot cells by conversion rate, requiring at least 50 listings per cell. Describe how you generate the grid and join listings to cells.

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40+ Airbnb-Specific Data Patterns Interview Questions & Answers (2026) | InterviewStack.io