Data Engineer

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

• Location: Delhi, India.