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Commerce, Travel & Marketplaces

Airbnb

Travel / marketplace · Data Engineering interview

Heavy multi-CTE SQL, interval/overlap coding, marketplace data modeling, and a culture-fit round that is a real dealbreaker.

Cloud / platform

AWS; Spark, Airflow (Airbnb-origin), Hive/Presto, Superset

Reported difficulty

Not reported

SQLPythonScalaSparkAirflow

Hiring process

Recruiter

Phone (coding + SQL)

Onsite loop (5–7)

Values/culture

Hiring manager

Offer

Round-by-round breakdown

SQL & Analytics

High confidence

Long queries with multiple CTEs, window functions, conditional aggregations
Top 5 hosts by total booking revenue in the past 90 days, excluding canceled bookings

Coding / Data Structures

Medium confidence

Detect conflicts and return overlapping windows (booking intervals)
Top-k hosts by nightly earnings
Python/Java/Scala allowed

Data Modeling

Medium confidence

Design schema for bookings, cancellations, payouts, penalties, availability calendars, pricing rules
Partition, scale, write SQL on the design

Values / Behavioral

Medium confidence

Airbnb values alignment (a genuine dealbreaker)

System & pipeline design prompts

Detect fraudulent bookings in real time
Hourly price-trend pipeline across major cities
Data warehouse / real-time streaming (L5+ leveling)

Prep — high priority

SQL (multi-CTE, window functions, conditional aggregation) under time pressure
Interval/overlap + top-k coding
One end-to-end ETL/streaming design; Airbnb values STAR stories

Prep — medium priority

Data modeling (marketplace schema, partitioning, SCD)
Spark internals
Airflow

Rejection patterns

Culture-fit rejection despite passing technically
Weak SQL under time pressure
Shallow system design at senior leveling
Messy interval/coding logic

Reported experiences

Senior Data Engineer

ReportedFirst-hand

TeamBlind "Airbnb Sr Data Engineer Interview" confirms the senior DE loop (body login-walled). Composite: recruiter → phone (coding + SQL) → loop (booking-interval coding, top-5-hosts SQL, marketplace schema, real-time fraud design, values, HM).

Rehearse for Airbnb

Answered questions on what Airbnb actually asks — each with the short answer, the answer that gets you rejected, and the follow-up.

Then build one and have it reviewed: design a daily orders pipeline or model a marketplace. No account.

All interview experiences

Openingloading the content…

Commerce, Travel & Marketplaces

Airbnb

Travel / marketplace · Data Engineering interview

Heavy multi-CTE SQL, interval/overlap coding, marketplace data modeling, and a culture-fit round that is a real dealbreaker.

Cloud / platform

AWS; Spark, Airflow (Airbnb-origin), Hive/Presto, Superset

Reported difficulty

Not reported

SQLPythonScalaSparkAirflow

Hiring process

Recruiter

Phone (coding + SQL)

Onsite loop (5–7)

Values/culture

Hiring manager

Offer

Round-by-round breakdown

SQL & Analytics

High confidence

Long queries with multiple CTEs, window functions, conditional aggregations
Top 5 hosts by total booking revenue in the past 90 days, excluding canceled bookings

Coding / Data Structures

Medium confidence

Detect conflicts and return overlapping windows (booking intervals)
Top-k hosts by nightly earnings
Python/Java/Scala allowed

Data Modeling

Medium confidence

Design schema for bookings, cancellations, payouts, penalties, availability calendars, pricing rules
Partition, scale, write SQL on the design

Values / Behavioral

Medium confidence

Airbnb values alignment (a genuine dealbreaker)

System & pipeline design prompts

Detect fraudulent bookings in real time
Hourly price-trend pipeline across major cities
Data warehouse / real-time streaming (L5+ leveling)

Prep — high priority

SQL (multi-CTE, window functions, conditional aggregation) under time pressure
Interval/overlap + top-k coding
One end-to-end ETL/streaming design; Airbnb values STAR stories

Prep — medium priority

Data modeling (marketplace schema, partitioning, SCD)
Spark internals
Airflow

Rejection patterns

Culture-fit rejection despite passing technically
Weak SQL under time pressure
Shallow system design at senior leveling
Messy interval/coding logic

Reported experiences

Senior Data Engineer

ReportedFirst-hand

Rehearse for Airbnb

Answered questions on what Airbnb actually asks — each with the short answer, the answer that gets you rejected, and the follow-up.

Then build one and have it reviewed: design a daily orders pipeline or model a marketplace. No account.

All interview experiences