Automated Data Governance Specialist

4 days ago

Dindigul, Tamil Nadu, India beBeeData Full time ₹ 15,00,000 - ₹ 20,00,000
Senior Data Pipeline Engineer

We are seeking a highly skilled Senior Data Pipeline Engineer to design, build, and maintain automated data pipelines supporting data governance and analytics initiatives.

The ideal candidate will have strong experience in Azure and Databricks, with proficiency in PySpark, SQL, and ETL/ELT pipeline design. They will be responsible for developing production-ready workflows for ingestion, transformation, quality checks, lineage capture, access auditing, cost usage analysis, retention tracking, and metadata integration.

  • Key Responsibilities:
  • 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.
  • 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.
  • 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.
  • Microsoft Purview Integration: Automate asset onboarding, metadata enrichment, classification tagging, and lineage extraction for integration into governance reporting.
  • Data Retention & Policy Enforcement: Implement logic for retention tracking and policy compliance monitoring (masking, RLS, exceptions).
  • Job & Query Monitoring: Build pipelines to track job performance, SLA adherence, and query costs for cost and performance optimization.
  • Metadata Storage & Optimization: Maintain curated Delta tables for governance metrics, structured for efficient dashboard consumption.
  • Testing & Troubleshooting: Monitor pipeline execution, optimize performance, and resolve issues quickly.
  • Collaboration: Work closely with the lead engineer, QA, and reporting teams to validate metrics and resolve data quality issues.
  • Security & Compliance: Ensure all pipelines meet organizational governance, privacy, and security standards.

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