AI Data Readiness Lead

Responsibilities

• Own the metric registry and establish canonical definitions for core business metrics.
• Resolve conflicting definitions by convening stakeholders, documenting disagreements, and driving decisions.
• Publish metric-definition changes with clear explanations of impact and rationale.
• Implement approved definitions in the semantic layer and data catalog; retire superseded versions.
• Audit reporting assets, retire unused assets, and establish ownership for retained assets.
• Build data-quality checks and agents covering freshness, uniqueness, referential integrity, and cross-system reconciliation.
• Route data-quality failures to named owners or agents for remediation.
• Maintain an inventory of AI agents accessing company data and the definitions they use.
• Evaluate AI-agent outputs against ground truth and track accuracy, refusal, and error rates.
• Enable trustworthy self-service access to governed data for people and AI tools.

Requirements

• 5+ years of experience in analytics, analytics engineering, or a closely related field.
• Strong SQL skills, including reverse-engineering undocumented transformation logic.
• Direct production ownership of a semantic or metrics layer using dbt, Cube, LookML, or an equivalent tool.
• Ability to resolve conflicting metric definitions across functions and reach decisions.
• Clear written communication and strong documentation skills.
• Comfort deprecating and removing work created by others.
• Experience evaluating LLM or AI-agent outputs against ground truth.
• Experience developing or contributing to data catalogs and/or lineage tooling.

Nice to Have

• Experience with lakehouse architectures, Apache Iceberg, Athena, Trino, or similar technologies.
• Exposure to audit readiness, SOX, or financial-controls environments.
• Experience with consumption- or usage-based business models.
• Experience joining a function early, before formal processes existed.

Compensation

• $165,000–$220,000 annual salary.