Senior Staff Data Engineer, Foundational Data

Airbnb 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 conne...

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 cl...

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 datase...

Provide technical leadership across the team's data engineering and analytics engineering work, spanning cloud cost, traffic & bots, and infrastructure intelligence
Define and own the multi-year data strategy and architecture for Airbnb's foundational data assets, and build the cross-org consensus needed to fund and execute it
Own how Airbnb measures its AI: the unit economics of training and serving our own models, GPU fleet utilization, and the cost and adoption of developer AI tooling
Design and build the datasets that integrate cost, utilization, performance, and reliability signals across Airbnb's infrastructure fleets, starting from a blank page
Stay hands-on in the pipelines and models you architect, close enough to the build to catch the problems design reviews miss
Influence and coach a distributed team of Data Engineers and Analytics Engineers, raising the quality bar through design and code review and scaling it by building the frameworks and automated checks other teams adopt
Navigate conflicting stakeholder requirements across Infrastructure, Finance, Data Science, and Product Engineering, and land a single definition everyone can build on
Identify and eliminate duplication and data fragmentation across the engineering organization, driving deprecation of what your models replace
Participate in the team's on-call rotation for the datasets we own

You do not need prior experience with cloud cost, infrastructure, or AI/ML data to succeed here. What we cannot teach is the following.
12+ years of relevant industry experience with a BS/Masters, or 9+ years with a PhD, in data engineering or a closely related field
You have designed, built, and operated production data pipelines at large scale, and you write strong SQL and Python. Scala experience is useful but not required
You can design a dimensional model and define a metric others will trust, even if analytics engineering has not been your job title
You have moved into an unfamiliar data or technical domain and become productive quickly, building enough understanding of the underlying systems to model them well
You have been the technical lead on a team or program, setting direction that other senior engineers executed against without formal authority over them, while still writing code yourself
You have written the multi-year technical strategy for an area, and got other organizations to fund and deliver against it
You have built a data product where no schema or precedent existed, and persuaded other teams to adopt it
You have written the document that aligned several teams on a contested technical decision, and you can explain a data model to a finance partner as readily as to an engineer