Data Engineer

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

• Location: Delhi.