Data Engineer

Own and maintain the AI governance data model, transformation pipelines, data quality, lineage, and reporting for executive and audit stakeholders.

Design and maintain metrics and KPIs that measure AI governance program performance, risk, and compliance, ensuring data and metrics can be defended under scrutiny.

Lead entity reconciliation between AI system registries and product security assets to ensure data integrity and drive exception reporting for follow-up actions.

4+ years experience in analytics engineering, data engineering, or BI engineering, including support for security, risk, compliance, or audit functions.

Strong proficiency in SQL (including complex queries and window functions) and Python (e.g., pandas) for data transformation and analysis.

Experience with data modeling, pipeline design (ETL/ELT), cloud data platforms (e.g., Snowflake, BigQuery), version control, and CI/CD for analytics code.

Experience building metrics and reports that have been audited or reviewed by executives or regulators and ability to defend those metrics.

Experienced in matching and reconciling entities across conflicting or multiple authoritative data sources dealing with data inconsistencies and ownership attribution.

Able to work with cross-functional teams to define data requirements and ensure metric definitions align with governance and audit needs before publication.

Skilled in designing analytics for security operations, governance, risk, compliance, and audit environments with an emphasis on trustworthiness, traceability, and evidential reporting.