Overview
Own the analytics engineering foundation for AI governance, including the data model, trusted pipelines, lineage, metrics, and reporting used by security, governance, audit, and executive stakeholders.
What you'll do
- Build and maintain the AI governance data model and transformation pipelines.
- Implement data quality checks, freshness monitoring, completeness monitoring, and end-to-end lineage.
- Reconcile AI registries and product security records across conflicting systems of record.
- Design metrics for registry coverage, risk, controls, exceptions, remediation, and service performance.
- Develop analytics for control coverage, failures, trends, drift, and remediation time.
- Build reporting for the Director, AI Governance Council, CISO, auditors, and board.
- Structure risk, incident, control, and exposure data for cyber risk quantification.
What you'll need
- 4+ years in analytics engineering, data engineering, or BI engineering.
- Experience supporting security, risk, compliance, or audit functions.
- Strong SQL, including window functions, complex joins, and query performance analysis.
- Working Python with pandas or equivalent for transformation and reconciliation.
- Experience with data modeling, ETL/ELT, incremental processing, and warehousing.
- Hands-on BI and visualization experience with Tableau, Power BI, Looker, or similar.
- Experience with cloud data platforms and version control or CI/CD for analytics code.
Nice to have
- Experience with security operations, GRC, vendor risk, or audit metrics.
- API-based ingestion from security and identity tooling.
- Data quality, observability, lineage, or catalog implementation experience.
- Exposure to AI governance or security frameworks.
- Experience with cyber risk quantification or board-level risk reporting.
Details
Read the full description and apply on the company’s own careers page.
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