We are seeking a hands-on Data Engineer to develop, optimize, and maintain automated data pipelines supporting data governance and analytics initiatives. This role will focus on building production-ready workflows for ingestion, transformation, quality checks, lineage capture, access auditing, cost usage analysis, retention tracking, and metadata integration, primarily using Azure Databricks, Azure Data Lake, and Microsoft Purview.
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.
Required Qualifications
Bachelor’s degree in computer science, Engineering, Information Systems, or related field
6+ 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
Microsoft Fabric, Azure Databricks, Azure Data Lake, Microsoft Purview, PySpark, SQL, ETL/ELT, Delta Lake, Azure Data Factory, Data Engineering, Data Quality, Data Governance, Data Lineage, Metadata Management, Data Cataloging, Data Modeling, Pipeline Development, Performance Tuning, Job Monitoring, Query Monitoring, Data Retention, Policy Enforcement, Security & Compliance, Classification Tagging, Access Auditing, Cost Optimization, Troubleshooting, Data Analytics