The posting, in Airbnb's own words
archived Oct 7, 2026Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.
The Community You Will Join:
At Airbnb, our mission is to create a world where anyone can belong anywhere. The Foundational Data team builds and operates the high-quality, widely reused datasets that power critical decisions across Airbnb: how visitors are measured from site traffic, how bot traffic is separated from organic traffic, and how cloud costs are attributed to Airbnb services. Our cost dashboards are among the most-used at Airbnb, relied on by technical leads, finance, and executives. Every dollar Airbnb spends running its infrastructure is attributed by models this team owns. We are now building a third pillar, and it puts this team at the forefront of how Airbnb measures AI: the cost, usage, and performance of the models we train and serve ourselves, and of the AI tools our own engineers use every day. When Airbnb asks whether an AI investment is paying off, the answer comes from datasets this team builds. Foundational Data sits inside Cloud Infrastructure and is a deliberate mix of Data Engineers and Analytics Engineers working as one team.
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The Difference You Will Make:
As a Senior Staff Data Engineer, you are the technical leader for this team and the person who sets its direction. This is a hands-on role with no direct reports: you will own the long-term data architecture and you will build against it, from the Airflow-orchestrated pipelines that ingest telemetry, logs, and billing data through to the dimensional models and Minerva definitions that thousands of people at Airbnb reason with. Because we are a mixed DE and AE team, you will work across both disciplines: this role needs data engineering depth, plus enough fluency in metric and dimensional modeling to set direction for the analytics engineering side. Infrastructure intelligence is where you will start, and it is fresh ground. The signals describing our fleets are fragmented across cost, utilization telemetry, service performance, and GPU and model telemetry. Nobody has modeled this coherently yet, and the AI side is the least charted part of all. There is no schema to inherit. You would decide what these datasets are, then bring the rest of the company along. This work carries regular visibility to directors and VPs across Infrastructure and Finance.