Lead Data Governance Specialist

2 days ago

Alwar, Rajasthan, India beBeeDataEngineer Full time ₹ 10,00,000 - ₹ 17,50,000
Job OverviewAzure Data Engineer – Data Governance & Pipelines

We are seeking an experienced Data Engineer to develop, optimize and maintain automated data pipelines supporting data governance and analytics initiatives. The successful candidate will have strong expertise in designing, building and deploying robust ETL/ELT pipelines in Databricks (PySpark, SQL, Delta Lake) to ingest, transform and curate governance and operational metadata from multiple sources landed in Databricks.

Key Responsibilities
  1. Pipeline Development – Design, build and deploy robust ETL/ELT pipelines in Databricks (PySpark, SQL, Delta Lake) to ingest, transform and curate governance and operational metadata from multiple sources landed in Databricks.
  2. Granular Data Quality Capture – Implement profiling logic to capture issue-level metadata (source table, column, timestamp, severity, rule type) to support drill-down from dashboards into specific records and enable targeted remediation.
  3. Governance Metrics Automation – Develop data pipelines to generate metrics for dashboards covering data quality, lineage, job monitoring, access & permissions, query cost, usage & consumption, retention & lifecycle, policy enforcement, sensitive data mapping and governance KPIs.
  4. Microsoft Purview Integration – Automate asset onboarding, metadata enrichment, classification tagging and lineage extraction for integration into governance reporting.
  5. Data Retention & Policy Enforcement – Implement logic for retention tracking and policy compliance monitoring (masking, RLS, exceptions).
  6. Job & Query Monitoring – Build pipelines to track job performance, SLA adherence and query costs for cost and performance optimization.
  7. Metadata Storage & Optimization – Maintain curated Delta tables for governance metrics, structured for efficient dashboard consumption.
  8. Testing & Troubleshooting – Monitor pipeline execution, optimize performance and resolve issues quickly.
  9. Collaboration – Work closely with the lead engineer, QA and reporting teams to validate metrics and resolve data quality issues.
  10. Security & Compliance – Ensure all pipelines meet organizational governance, privacy and security standards.

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