
Data Governance Engineer
2 days ago
Job Summary:
Data is the lifeblood of any organization, and ensuring its quality, security, and compliance with governance standards is a top priority. As a Data Governance Engineer, you will play a critical role in designing, building, and deploying robust data pipelines that meet these requirements.
The ideal candidate will have a strong background in data engineering, experience with Azure Databricks and Azure Data Lake, and proficiency in PySpark, SQL, and ETL/ELT pipeline design. They will also possess excellent problem-solving skills, be able to debug complex issues, and have a strong understanding of data modeling, metadata structures, and data cataloging concepts.
About the Role:- Design and Build Data Pipelines: Develop ETL/ELT pipelines in Databricks (PySpark, SQL, Delta Lake) to ingest, transform, and curate governance and operational metadata from multiple sources.
- 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.
- Education: Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
- Experience: 4+ years of hands-on data engineering experience, with Azure Databricks and Azure Data Lake.
- Skills: Proficiency in PySpark, SQL, and ETL/ELT pipeline design, demonstrated experience building granular data quality checks, integrating governance logic into pipelines, working knowledge of Microsoft Purview for metadata management, lineage capture, and classification, experience with Azure Data Factory or equivalent orchestration tools.
- Collaborative Environment: Work closely with the lead engineer, QA, and reporting teams to validate metrics and resolve data quality issues.
- Professional Growth: Opportunities for professional growth and development in a dynamic and challenging environment.
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