Analytics Engineer
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- Build trusted warehouse models and canonical metrics that match real business questions
- Ship tested, versioned analytics products and self-service decision experiences
- Operate observable semantic systems and extend trusted analytics into AI-native interfaces
Benchmark: OpenAI GTM Analytics + Airbnb Staff Analytics + Lovable Analytics Engineering roles (current Sep 2026) Market refresh: dimensional models/pipelines; canonical metrics and semantic layers; tested/versioned SQL; self-service BI and storytelling; lineage/observability; AI-assisted development; governed conversational analytics and custom data apps.
Who’s Hiring Right Now
US openings posted in the last 7 days at Airbnb — this role’s own benchmark employers — refreshed daily, as of 2026-11-01. Pulled directly from each company’s public job board; not exhaustive, and postings close fast — worth confirming the details on the employer’s own site before applying.
Enrichment
Build trusted warehouse models and canonical metrics that match real business questions
SQL, dimensional modeling + transformation grain
Coverage: SQL & Analytics / Data Modeling / Warehousing / Lakehouse
Canonical metrics, semantic layers + stakeholder discovery
Coverage: KPIs & Product Metrics / Semantic Layer / Product Discovery
Ship tested, versioned analytics products and self-service decision experiences
dbt-style transformations, tests + CI/CD
Coverage: Analytics Engineering / Git / Unit & Data Tests / CI-CD / Data Contracts
Self-service BI, analysis + decision storytelling
Coverage: BI / Product Analytics / Visualization / Data Storytelling
Operate observable semantic systems and extend trusted analytics into AI-native interfaces
Data quality, lineage + production observability
Coverage: Data Quality / Lineage / Observability / SRE / Governance
AI-ready analytics, conversational BI + custom data apps
Coverage: Semantic Systems / AI-Assisted Analytics / Streamlit / Conversational BI
Role-Readiness Proof / Capstone
Build and operate a production analytics product for a GTM, product or operations domain. Start from stakeholder decisions and source/event contracts; design facts and dimensions at explicit grain; implement SQL/dbt-style transformations in Git; define canonical metrics and a semantic layer; add unit, data and freshness tests plus CI; publish a governed self-service dashboard and decision memo; instrument lineage, ownership and data-quality alerts; expose one AI/conversational analytics experience constrained to trusted semantics plus one lightweight custom data app; document cost/performance and change-management tradeoffs; then use adoption and issue telemetry to prioritize the next data-product iteration.
Proof Rubric
- Demonstrate reproducibility and product judgment: correct grain and model design
- canonical metric definitions
- tested/versioned transformations
- lineage and freshness evidence
- self-service without metric drift
- a persuasive decision narrative
- clear ownership/SLOs
- governed AI queries grounded in trusted semantics
- maintainable custom-app code
- and a traceable link from business question to data contract to product outcome