Staff Analytics Engineer

📋 Description

• • Architect and own CodeRabbit’s BigQuery warehouse as the canonical analytical layer, building on the existing GCP and Fivetran foundation to achieve investor-grade governance and reliability.
• • Design and ship the dbt project end to end, from raw data ingestion through staging and intermediate models to consumer-facing marts, adhering to modern layering, testing, and version-control best practices.
• • Define and enforce canonical definitions of ARR, NRR, bookings, and customer lifecycle metrics within the dbt Semantic Layer to ensure consistency across BI tools, Salesforce, and AI agents.
• • Build and maintain the canonical revenue models that underpin CodeRabbit’s full billing structure, including seat subscriptions, usage-based add-ons, and per-marketplace settlements across AWS, GCP, Vercel, and other channels.
• • Design and implement the identity-resolution spine that unifies accounts and individuals across product, marketing, billing, and CRM systems using stable, system-generated identifiers.
• • Partner with Growth Engineering to develop and deploy GTM intelligence layers, including PQL and PQA scoring models and expansion signals that feed into reverse ETL workflows and tools like Clay to surface sales-ready accounts.
• • Enable the warehouse to serve as a first-class interface for AI agents by exposing the dbt Semantic Layer and Agents Schema as the governed source for agent queries, with PQA scoring trained using BigQuery ML and Vertex AI.
• • Own end-to-end data governance, including PII protection, consent management, and suppression models that control downstream data activation and compliance.
• • Establish and document company-wide data practices, definitions, and standards to serve as the trusted technical partner to Finance, RevOps, Marketing, and Product teams.
• • Translate complex modeling tradeoffs and metric definitions into clear, actionable insights for both technical peers and non-technical stakeholders including GTM leaders and Finance executives.
• • Build scalable data infrastructure that balances immediate business needs with long-term architectural integrity, ensuring the system remains agent-ready and adaptable to future AI integrations.
• • Maintain and evolve the data pipeline from ingestion configuration through business logic implementation to reporting outputs, ensuring accuracy, performance, and observability at every layer.
• • Collaborate cross-functionally to align data models with revenue goals, product usage patterns, and customer lifecycle stages, ensuring data directly drives pipeline growth and retention outcomes.
• • Ensure all analytical outputs are traceable, auditable, and governed, with clear documentation of lineage, transformation logic, and ownership for every model and metric.
• • Proactively identify gaps in data coverage or model accuracy that impact revenue decisions and lead initiatives to close them with engineering and product teams.
• • Serve as the primary technical owner of the analytics stack, setting the direction for tooling, standards, and practices that scale with the company’s growth and AI ambitions.