Data Engineer

Overview

Build and own the governance data layer for enterprise AI, including data models, pipelines, quality, lineage, metrics, and reporting used by security, governance, auditors, and executive stakeholders.

What you'll do

• Build transformation pipelines and analytical models for AI systems, risks, controls, incidents, vendors, and usage.
• Implement data quality checks, freshness monitoring, completeness monitoring, and end-to-end lineage.
• Reconcile AI registries and product asset records across conflicting systems of record.
• Design metrics for governance coverage, risk, controls, exceptions, remediation, and service performance.
• Analyze control results, failures, trends, drift, and remediation timelines.
• Build executive, governance council, CISO, board, audit, and compliance reporting.
• Structure risk, incident, control, and exposure data for AI 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 and working Python skills, including pandas or equivalent.
• 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.
• Applied experience defending metrics to demanding audiences and reconciling conflicting systems of record.

Nice to have

• Experience with security operations, GRC, vendor risk, audit, or related metrics.
• Exposure to AI governance, security frameworks, data lineage, or cyber risk quantification.
• Experience with AI asset inventories, AIBOMs, agent registries, or non-human identity data.

Details

• Location: Bengaluru