Data Engineer

Overview

Own the analytics engineering foundation for AI governance, including the data model, pipelines, quality, lineage, metrics, and reporting used by security, governance, audit, and board 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 registry and product security records across conflicting systems of record.
• Design metrics covering registry coverage, risk, controls, exceptions, remediation, and service performance.
• Analyze control results, failure trends, drift, and remediation timelines.
• Build reporting for the Director, AI Governance Council, CISO, and board.
• Structure risk, incident, control, and exposure data for cyber risk quantification and audit evidence.

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 reasoning.
• Working Python with pandas or equivalent for transformation, reconciliation, and analysis.
• Experience with ETL/ELT, data modeling, incremental processing, and warehousing.
• Hands-on BI experience with Tableau, Power BI, Looker, or similar.
• Experience defending metrics to demanding audiences and reconciling conflicting systems of record.

Nice to have

• Security operations, GRC, vendor risk, or audit metrics experience.
• API-based ingestion from security and identity tools.
• Data quality, observability, lineage, or catalog implementation experience.
• Exposure to AI governance or security frameworks.
• Cyber risk quantification or board-level risk reporting experience.
• AI asset inventory, AIBOM, agent registry, or non-human identity experience.

Details

• Location: Pune.
• Requires discretion and careful handling of sensitive security and risk data.