Overview
Own the analytics engineering foundation for AI governance, including data modeling, pipelines, quality, 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 AI-bill-of-materials records across systems of record.
- Design metrics covering registry coverage, risk, controls, exceptions, remediation, and service performance.
- Analyze control results, failure trends, drift, and time to remediation.
- Build executive, governance council, CISO, board, audit, and compliance reporting.
- Partner with data, security, identity, IT, procurement, and governance stakeholders on definitions and reporting.
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 performance analysis.
- Working Python, including pandas or equivalent, for transformation and reconciliation.
- Experience with ETL/ELT, data modeling, 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.
- Exposure to AI governance, security frameworks, AI asset inventories, or AIBOM concepts.
- Experience with cyber risk quantification or board-level risk reporting.
- Experience supporting IPO readiness, SOX, or investor due diligence.
Details
Read the full description and apply on the company’s own careers page.
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