• Own the enterprise data model: • Define and maintain canonical entities (Account, Customer, Location, Usage, Invoice, etc.) and their relationships across systems • Drive alignment between product, analytics, finance, and marketing data domains • Architect and maintain the dbt semantic layer: • Build modular, tested, and versioned dbt models with rigorous standards for naming, documentation, and lineage • Manage exposures to ensure all metrics and dashboards trace back to tested sources • Govern KPI and metric definitions: • Partner with Finance and BI to define and codify key company metrics (ARR, NRR, CAC payback, etc.) and enforce change control through versioned definitions • Enforce data contracts and schema governance: • Define and validate schemas, event structures, and data types for all inbound systems • Implement CI/CD tests to block breaking changes and maintain cross-system consistency • Drive observability and data quality standards: • Integrate dbt tests and freshness SLOs with the data catalog • Implement automated monitoring and alerting for data breaks and policy violations • Build the bridge between data and compliance: • Collaborate with Legal, IT, and Internal Audit to ensure IPE (information produced by the entity) lineage, evidence retention, and SOX readiness • Mentor and multiply: • Set technical direction and review standards in close partnership with the broader data organization • Define reusable macros, patterns, and documentation conventions that raise the bar for quality and reliability • Partner cross-functionally: • Work closely with data engineering on ingestion and contracts, BI on dashboard alignment, and Finance on KPI integrity • Influence how new systems and features are instrumented at the source to keep the data layer consistent