Chief Cloud Data Architect

4 days ago

Jamnagar, Gujarat, India beBeeData Full time ₹ 1,50,00,000 - ₹ 2,01,00,000
Cloud Data Engineering Lead

We are seeking a highly skilled data engineer to lead the development, optimization, and maintenance of automated data pipelines supporting enterprise 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 using cloud-based technologies such as 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 engineering team, 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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