Data Engineer

Job title : Data Engineer Experience: 6+ years Location: Chennai Shift: US Eastern Time ( 5:00 PM – 2:00 AM ) Requirements 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. Experience: 6+ years in data engineering, with strong Azure and Databricks experience 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.