Data Engineer

Own and maintain the AI governance analytical data model and transformation pipelines ensuring traceability and data quality for executive and audit reporting.

Design and implement automated data quality checks, lineage tracing, reconciliation between AI system registers and product security records, and generate exception reports.

Build, maintain, and explain metrics, KPIs, and reports on AI risk, controls, compliance, and incidents to support decision making for the board, CISO, and AI Governance Council.

4+ years in analytics engineering, data engineering, or BI engineering with some related to security, risk, compliance, or audit.

Strong SQL skills including window functions and performance optimization; hands-on Python experience for data transformation and analysis.

Experience with data modeling, pipeline design (ETL/ELT), cloud data platforms (Snowflake, BigQuery, Redshift, Databricks, or similar), and BI/visualization tools (Tableau, Power BI, Looker or similar).

Work Experience Required: 4+ years in relevant analytics/data engineering roles explicitly supporting security or compliance functions.

Demonstrated history of building and defending metrics and reports to demanding audiences including external auditors, regulators, executive committees, or boards.

Proven experience in entity reconciliation across multiple conflicting authoritative systems, handling identity resolution, ownership attribution, and stale record resolution.

Experience working with security operations, GRC, audit programs, AI governance frameworks (e.g., ISO 42001, EU AI Act), or cyber risk quantification methodologies, with ability to deliver audit-ready, traceable data reporting.