Data Governance & Metrics Engineer

Analytics is only as trustworthy as the definitions underneath it. As more reporting and decision-making moves to AI agents, the cost of ambiguous or conflicting metric definitions compounds—an agent applies the wrong rule confidently, at scale, and nobody catches it.

This role exists to prevent that. You will own what our numbers mean, make those definitions enforceable in the systems that serve them, and verify that both people and agents are using them.

This is a governance-first role with real technical depth. You will spend your time defining, implementing, and validating, building pipelines and developing agentic reporting are secondary.

What you'll work on

Own the metric registry. Establish canonical definitions for the metrics the business runs on. Where competing versions exist, convene the owners, document the disagreement, and drive to a decision. Publish changes with a clear statement of what moves and why.

Make definitions enforceable. Implement agreed definitions in the semantic layer and data catalog so they are applied by the system rather than described in a document. Retire superseded versions.

Reduce the surface area. Audit the reporting estate, retire assets with no audience, and establish ownership for what remains.

Build data quality checks/agents. Freshness, uniqueness, referential integrity, and cross-system reconciliation—with failures routed to named owners/agents who act on them.

Verify AI agents. Maintain an inventory of agents accessing company data and the definitions each relies on. Evaluate agent output against known-correct answers and track accuracy, refusal, and error rates.

Enable self-serve. Make governed data accessible and trustworthy for people querying it directly or through AI tools.

Qualifications

• 5+ years in analytics, analytics engineering, or a closely related field

• Strong SQL, including comfort reverse-engineering undocumented transformation logic written by others

• Direct ownership of a semantic or metrics layer in production—dbt, Cube, LookML, or equivalent. Not just usage: responsibility for what went into it and why

• Demonstrated ability to resolve conflicting metric definitions across functions and land a decision

• Clear written communication. Most of your output is documentation others must trust without re-deriving it

• Comfort deprecating and removing work that others built

• Experience evaluating LLM or AI agent output against ground truth

• Experience developing or contributing to a data catalog and/or lineage tooling

Nice to have

• Experience with lakehouse architectures, Iceberg, Athena, Trino, or similar

• Exposure to audit readiness, SOX, or financial controls environments

• Consumption or usage-based business models, where committed, consumed, invoiced, and recognized revenue are genuinely different numbers

• Having joined a function early, before process existed