Data Engineer

Delhi · Pune · 4+ years experience · Posted Sep 28, 2026

At a glance

You will build the data layer that governs AI across the enterprise, powering risk, compliance, and board-level decision-making.
This is an analytics engineering role focused on owning the governance data model, transformation pipelines, data quality, lineage, and metrics.
You will work alongside an AI Security Analyst and an AI Security Automation Engineer, reporting to the Director.

Own the analytical data model for AI governance including AI systems and agents, owners, risk classifications, assessments, findings, exceptions, control results, incidents, vendors, and usage.
Build and maintain the transformation layer that turns raw landed data into the governance model, with tests, version control, and CI.
Build automated data quality checks, freshness and completeness monitoring, and end-to-end lineage tracing reported figures back to source systems.
Own scheduled reconciliation between the AI System and Agent Registry and Product Security records, matching entities across sources and resolving conflicts.
Design and own the metric set measuring the program such as registry coverage, shadow AI trends, risk tier distribution, and control effectiveness.
Own analytics on top of control checks, covering failure rates, drift detection, time-to-remediation, and reporting to the governance council.
Build and maintain executive, council, and board reporting with visual and narrative clarity.
Structure risk, incident, control, and exposure data to support AI cyber risk quantification.
Own data underpinning ISO 42001 certification, EU AI Act readiness, and internal AI impact assessments.
Partner with Enterprise Data and Analytics, Product Security, SecOps, Identity, Secure IT, and Procurement on platform placement, semantic definitions, and data validation.

4+ years in analytics engineering, data engineering, or BI engineering, with at least some of it supporting security, risk, compliance, or audit functions
Strong SQL, including window functions, complex joins, and reasoning about query performance at scale
Working Python (pandas or equivalent) for transformation, reconciliation, and analysis
Data modeling and pipeline design experience including ETL/ELT, dimensional or equivalent modeling, incremental processing, and warehousing concepts
Hands-on BI and visualization work using tools like Tableau, Power BI, or Looker
Cloud data platform experience such as Snowflake, BigQuery, Redshift, or Databricks
Version control and CI/CD for analytics code using Git, dbt, GitHub Actions, or equivalent
Demonstrated applied experience building metrics consumed by a demanding audience that held up under scrutiny from external auditors, regulators, or a board
Demonstrated applied experience in entity reconciliation across conflicting systems of record with incomplete, contradictory, or decaying data
Discretion and careful handling of sensitive data regarding security findings, incidents, control failures, and risk exposure

Experience building metrics or dashboards for security operations, GRC, vendor risk, or audit programs
API-based data ingestion from security and identity tooling (SIEM, CASB, IAM, EDR, cloud audit logs)
Data quality and observability tooling, and lineage or catalog implementation
Exposure to AI governance frameworks (ISO 42001, NIST AI RMF, EU AI Act risk tiers) or security frameworks (NIST CSF, SOC 2)
Familiarity with cyber risk quantification methodologies such as FAIR, or experience supporting board-level risk reporting
Exposure to AI asset inventory and AIBOM concepts, agent registries, or non-human identity data
Experience supporting IPO readiness, SOX, or investor due diligence from a data and reporting perspective

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