
Principal Data Engineer
1 day ago
Overview:
Develop, optimize and maintain scalable data pipelines to support business intelligence and analytics initiatives.
Description:
Key Responsibilities- Pipeline Development: Design, build and deploy robust ETL/ELT pipelines in Azure 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.
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
- 4+ years of hands-on data engineering experience, with Azure Databricks and Azure Data Lake.
- Proficiency in PySpark, SQL, and ETL/ELT pipeline design.
- Demonstrated experience building granular data quality checks and 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.
- Understanding of data modeling, metadata structures, and data cataloging concepts.
- Strong debugging, performance tuning, and problem-solving skills.
- Ability to document pipeline logic and collaborate with cross-functional teams.
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