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Salary: USD 248,000 - 310,000 / annual
Airbnb's Foundational Data team builds and operates high-quality, widely reused datasets that power critical decisions across the company. This includes visitor measurement from site traffic, bot detection, and cloud cost attribution—datasets relied upon by technical leads, finance, and executives across the organization.
As a Senior Staff Data Engineer, you are the technical leader for this team with no direct reports. You will own the long-term data architecture and build against it, from Airflow-orchestrated pipelines ingesting telemetry, logs, and billing data through to dimensional models and metric definitions used by thousands at Airbnb. This is a hands-on role requiring both deep data engineering expertise and fluency in metric and dimensional modeling to set direction for the analytics engineering side of the team.
Your primary focus will be infrastructure intelligence—a new frontier where signals describing Airbnb's fleets are currently fragmented across cost, utilization telemetry, service performance, and GPU/model telemetry. You will decide what these datasets are and bring the rest of the company along. This includes owning how Airbnb measures AI: the unit economics of training and serving its own models, GPU fleet utilization, and the cost and adoption of developer AI tooling.
Key responsibilities include:
- Provide technical leadership across 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, building cross-org consensus to fund and execute it
- Design and build datasets integrating cost, utilization, performance, and reliability signals across infrastructure fleets from a blank page
- Stay hands-on in the pipelines and models you architect, close enough to catch problems design reviews miss
- Influence and coach a distributed team of Data Engineers and Analytics Engineers through design and code review
- Navigate conflicting stakeholder requirements across Infrastructure, Finance, Data Science, and Product Engineering to land unified definitions
- Identify and eliminate duplication and data fragmentation across the engineering organization
- Participate in the team's on-call rotation for owned datasets
This role carries regular visibility to directors and VPs across Infrastructure and Finance.
REQUIREMENTS:
- 12+ years of relevant industry experience with a BS/Masters, or 9+ years with a PhD, in data engineering or a closely related field
- Designed, built, and operated production data pipelines at large scale; strong SQL and Python skills (Scala useful but not required)
- Ability to design dimensional models and define metrics others will trust, even if analytics engineering has not been your job title
- Experience moving into unfamiliar data or technical domains and becoming productive quickly, building sufficient understanding of underlying systems to model them well
- Experience as a technical lead on a team or program, setting direction that other senior engineers executed against without formal authority, while still writing code yourself
- Written multi-year technical strategy for an area and got other organizations to fund and deliver against it
- Built a data product where no schema or precedent existed and persuaded other teams to adopt it
- Ability to write documents aligning several teams on contested technical decisions and explain data models to both finance partners and engineers
