Description

Key Requirements

• 12–18 years of overall data engineering experience
• 8+ years of experience in enterprise Data Warehouse and Data Lake platforms
• 5+ years of hands-on experience with Databricks and Spark at scale
• Strong experience in modernizing legacy Cloudera platforms (CDH/CDP, Hive, HBase, Impala, Spark) to Databricks Lakehouse
• Redesign ingestion, transformation, and consumption patterns from HDFS-based architecture to cloud object storage and Delta Lake
• Refactor legacy Hive/Impala logic into PySpark and Spark SQL ELT pipelines
• Ensure data reconciliation, audit integrity, and consistency during migration
• Design and govern enterprise Data Warehouse and Data Lake/Lakehouse architectures
• Implement layered architecture including Raw/Landing, Curated/Conformed, and Semantic/Consumption layers
• Modernize traditional EDW platforms into scalable lakehouse architectures
• Strong experience in finance and risk data models including General Ledger, Sub-ledger, financial hierarchies, and risk exposure models (credit, liquidity, market risk)
• Enable reporting use cases including aggregation, drill-down, and drill-back capabilities
• Build and manage semantic/consumption layers for BI, reporting, and analytics
• Define business metrics, dimensions, hierarchies, and KPIs
• Experience with Databricks SQL, Delta tables, and dbt or similar frameworks
• Develop and optimize large-scale data pipelines using PySpark, Spark SQL, and Delta Lake
• Implement Medallion architecture (Bronze, Silver, Gold layers)
• Optimize workloads using Z-ORDER, OPTIMIZE, caching, and cluster configurations
• Implement data governance, data quality frameworks, reconciliation controls, and exception handling
• Establish data lineage and metadata management
• Ensure data security, access control, and compliance standards
• Experience with cloud platforms such as AWS or Azure
• Experience with CI/CD pipelines using Git, Terraform, Jenkins, or Azure DevOps
• Familiarity with orchestration tools such as Airflow or Databricks Workflows
• Experience with dbt is a plus
• Act as a technical authority and lead architecture decisions
• Mentor and guide senior engineers and establish engineering standards
• Strong stakeholder management with finance, risk, analytics, and governance teams
• Ability to translate complex data structures into business-ready insights

Nice to Have

• Experience in BFSI, Capital Markets, or regulatory reporting
• Exposure to SAP Finance, Oracle Financials, or S/4HANA
• Experience supporting AI/ML workloads
• Databricks or cloud certifications

Key Skills

Education

Bachelor's degree