Overview
Own the analytics engineering foundation for AI governance, including the data model, transformation 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 layer.
- Implement data quality checks, monitoring, and end-to-end lineage.
- Reconcile AI registries and product asset or AIBOM systems.
- Design metrics covering governance coverage, risk, controls, exceptions, and remediation.
- Analyze control-monitoring results, trends, drift, and remediation timelines.
- Produce reporting for the Director, AI Governance Council, CISO, and board.
- Structure risk and compliance data for audits, AI risk quantification, 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 query-performance reasoning.
- Working Python with pandas or equivalent.
- Experience with ETL/ELT, data modeling, incremental processing, and warehousing.
- Hands-on BI or visualization experience with Tableau, Power BI, Looker, or similar.
- Demonstrated experience defending metrics and reconciling conflicting systems of record.
Nice to have
- Security operations, GRC, vendor risk, or audit reporting experience.
- Security-tool API ingestion, data observability, or lineage implementation experience.
- Exposure to AI governance or security frameworks and cyber risk quantification.
- Experience with AI asset inventories, AIBOMs, agent registries, or non-human identities.
Details
Read the full description and apply on the company’s own careers page.
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