Original listing text, shown exactly as published by the company.
The Opportunity
We're looking for our Staff Analytics Engineer to build the BigQuery and dbt foundation behind CodeRabbit's go-to-market, and the intelligence that runs on it. You'll architect the warehouse, identity spine, and semantic layer that give every team one trusted definition of ARR, NRR, and lifecycle, then turn that foundation into revenue: PQL and PQA scoring that surface product-qualified accounts the moment they're ready, and expansion signals that catch accounts growing into their next tier. The models you ship move pipeline and retention, not just dashboards. You'll define the canonical model rather than inherit one, on a GCP-native platform we're building to be agent-ready from day one.
Key Responsibilities
- Architect and own CodeRabbit's BigQuery 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 CodeRabbit'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 BigQuery 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, RevOps, Marketing, and Product.
Qualifications
- Deep, hands-on analytics engineering experience: expert dbt (project architecture, testing, semantic layer) and strong warehouse fluency, with hands-on BigQuery 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 BigQuery 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.
Preferred Qualifications
- 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.
Target salary for this role is $240k-$250k, plus equity. Actual salary will be based on job-related skills, experience, and location.
