Staff Analytics Engineer

Responsibilities

• Architect and own Code Rabbit's Big Query warehouse as the canonical analytical layer, building on the existing GCP and Fivetran foundation and taking it to a governed, investor-grade standard.
• Design and ship the dbt project end to end, from raw sources through staging and intermediate models to consumer-facing marts, following modern layering and version-control best practices.
• Set the canonical definitions of ARR, NRR, bookings, and lifecycle in the dbt Semantic Layer so BI, Salesforce, and AI agents all read the same number.
• Build the canonical revenue models behind Code Rabbit's full billing model: seat subscriptions, usage-based add-ons, and per-marketplace settlement across our marketplace channels (AWS, GCP, Vercel, and others).
• Build the identity-resolution spine that resolves a single account and person across product, marketing, billing, and CRM, anchored on stable, system-generated identifiers.
• Partner with Growth Engineering to ship the GTM intelligence layer: PQL and PQA scoring, expansion signals, and the single sales-ready queue that reaches reps through reverse ETL and tools like Clay.
• Make the warehouse a first-class interface for AI agents, exposing the semantic layer and Agents Schema as the governed source agents query, with PQA scoring trained in Big Query ML and Vertex AI.
• Own data governance, including PII protection and a consent and suppression model that gates downstream activation.
• Establish the data practices, definitions, and documentation the company runs on, and serve as the trusted technical partner to Finance, Rev Ops, Marketing, and Product.

• Deep, hands-on analytics engineering experience: expert dbt (project architecture, testing, semantic layer) and strong warehouse fluency, with hands-on Big Query and GCP a strong plus.
• Strong SQL and data modeling judgment: dimensional modeling, grain discipline, and a clear sense of when to compute versus store aggregations.
• A track record building the models a revenue team acts on, spanning canonical financial metrics (ARR, NRR, bookings, cohort retention) and the GTM scoring and lifecycle layers that activate through reverse ETL.
• An appetite to build AI-native: comfort applying Big Query ML and Vertex AI to scoring, with a point of view on the semantic layer as the governed interface AI agents query.
• Ownership instinct across the full stack, from ingestion config through business logic to reporting, with the judgment to know when to build for now versus build to scale.
• Strong written and verbal communication; you can make a metric definition or a modeling tradeoff clear to Finance, GTM leaders, and technical peers alike.
• At least 6 years of progressive experience in analytics engineering, data engineering, or a closely related data role, including time as the senior technical owner of a warehouse or dbt project.
• Experience with identity resolution across disconnected systems, ideally in a PLG / product-led enterprise (PLE) motion.
• Developer-tools or technical B2B SaaS background, and comfort working agent-first with tools like Claude Code.