Jobs / United States / Airbnb

Airbnb

Staff Data Engineer, Tech Foundations

San Francisco, CA Posted today

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Skills this job asks for

JiraLeadershipData engineeringReconciliation

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Sponsorship verdict

Sponsorship possible

One solid signal, not two. Worth applying, and worth asking about sponsorship early.

  • Employer is on a government sponsor recordThe US Department of Labor certified 167 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first.
  • The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
  • No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area.
  • What Airbnb paid sponsored hires in similar roles2 certified filings for “Data Engineer” (Statisticians) in CA: $162k–$179k, median $171k.
  • Confirmed live todayWhen a source last listed this job as open.
Details and sources
Employer is on a government sponsor record
The US Department of Labor certified 167 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. Source: LCA disclosure data (US Department of Labor (OFLC)).
No salary bar for this route
H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
What Airbnb paid sponsored hires in similar roles
2 certified filings for “Data Engineer” (Statisticians) in CA: $162k–$179k, median $171k. Most were filed at wage level II (50%) — 2 lottery entries, ≈31% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).

US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).

A verdict summarises public evidence. It is not legal advice and never a guarantee: the employer and the immigration authority decide. Sign in to factor in where you can already work.

About the role

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 connect with communities in a more authentic way. Staff Data Engineer, Enterprise Core Datasets, Tech Foundations The Community You Will Join: At Airbnb, trustworthy data powers business decisions, product innovation, and operational efficiency. The Enterprise Core Datasets team builds the technical data foundations that help Airbnb understand how the business operates, where resources are invested, and how new technologies create value. Our scope spans high-impact datasets across the organization: measuring AI adoption, usage, and cost; understanding revenue, costs, and profitability through Unit Economics; integrating non-platform data from Jira and other enterprise applications; and powering critical company metrics such as Gross Booking Value and Revenue. You will join a team that values thoughtful architecture, engineering rigor, and operational ownership. We collaborate with engineering, product, analytics, finance, and business teams to turn complex requirements into dependable, reusable data assets. We welcome strong engineers who are curious about the business and eager to solve problems across diverse domains. The Difference You Will Make: As a Staff Data Engineer, you will provide hands-on technical leadership for datasets that shape decisions across Airbnb. Your work will help the organization measure AI adoption and spending, understand the economics of its products, and connect enterprise application data to meaningful business and operational insights. You will lead complex initiatives from problem definition and architectural design through implementation, validation, and production operations. You will build extensible data foundations that accommodate new AI tools, products, cost categories, and enterprise systems while maintaining clear definitions, traceability, and reliable historical data. Operating in an ecosystem that includes petabyte-scale datasets, you will improve correctness, freshness, performance, and cost efficiency. You will develop reusable frameworks that simplify source onboarding, reduce duplicated logic, and make critical data easier to discover and use. Through mentorship and contributions to Airbnb’s shared data engineering tools and standards, your impact will extend across the data community. A Typical Day: • Lead the architecture, implementation, and operation of high-impact data pipelines across AI usage and cost, Unit Economics, enterprise applications, and critical Financial metrics. • Partner with stakeholders to define how AI adoption, consumption, and spending are measured across the organization, and build consistent data models that support those definitions. • Evolve Unit Economics datasets to connect revenue and diverse cost drivers, enabling teams to understand product profitability and resource efficiency. • Build reliable ingestion and transformation pipelines for non-platform systems such as Jira and other BizTech enterprise applications. • Translate ambiguous requirements into clear source contracts, reusable fact and dimension models, and technical designs. • Develop configurable pipelines and shared components that simplify onboarding new data sources, products, and business requirements. • Establish automated testing, reconciliation, anomaly detection, and monitoring to protect data accuracy, completeness, and availability. • Lead safe migrations and historical backfills through dependency analysis, validation, controlled cutovers, and recovery pl

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