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# Senior Staff Data Engineer, Foundational Data

- **Company:** Airbnb
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $248,000.0 - $310,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Data Analysis, Code Review, Data Architecture, Information Engineering, Dimensional Modeling, Python (Programming Language), Operational Databases, Standard Sql, Data Strategy, Build Management, Software Coding
- **Published:** October 26, 2026
- **Apply:** https://www.adzuna.com/details/5896569358

## About the Role

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

## Description

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.
A Typical Day:

 * 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