Member of Data Staff (Analytics Engineer)

Join our team as an Analytics Engineer and help build the foundation for an AI-native data organization. In this role, you will design core data models, pipelines, and governance practices that power the entire company. You will also ensure that these systems are secure, privacy-aware, and accessible to AI agents and data scientists. This position requires a deep understanding of SQL, data modeling, warehouse management, and governance practices.

• Design and maintain high-quality data models, marts, and pipelines that make analysis fast, reliable, and reusable.
• Own warehouse architecture, environments, permissions, performance, cost, data lifecycle, and operational hygiene so the platform scales cleanly.
• Define and champion dbt patterns, dimensional modeling practices, naming conventions, tests, and review processes.

• Pipeline ownership - you've built, maintained, debugged, and improved production data pipelines
• Autonomy and execution - you can take projects from ambiguous problem to production-quality system with minimal oversight
• 6+ years of experience as an analytics engineer, data engineer, data scientist, or closely related role
• Deep SQL expertise - you can reason about correctness, performance, joins, grain, and edge cases in complex warehouse queries
• Strong data modeling experience - you've worked hands-on with dbt (or a similar transformation framework) in production, and you understand dimensional modeling, data contracts, testing, and how analytical schemas should evolve
• Stakeholder fluency - you know how to turn messy analytical requirements into trusted models, metrics, and reusable data assets
• Warehouse management experience - you've worked with warehouse administration, access patterns, permissions, performance tuning, cost management, or operational ownership
• Governance mindset - you think clearly about data ownership, access controls, privacy, retention, lineage, auditability, and the risks of making data too easy to access
• Operational judgment - you care about reliability, governance, security, cost, and long-term maintainability
• AI-native working style - you already use AI to speed up development, documentation, QA, exploration, and repetitive workflow automation
• Experience with RBAC, PII handling, data classification, retention policies, audit workflows, or privacy/security reviews
• Experience with Databricks or other modern data infrastructure
• Snowflake administration, optimization, cost management, or warehouse performance tuning
• Python experience for data tooling, automation, orchestration, or quality checks
• Previous experience as an early analytics engineer or data engineer at a high-growth startup
• Experience building semantic layers, metrics layers, metadata systems, or data catalogs