Databricks Engineer (PySpark and Data Lake)

Overview

We are seeking a Data Engineer to lead the modernization of legacy ETL systems by migrating Ab Initio workflows to scalable, modular PySpark pipelines on Databricks. The role involves transforming complex data ecosystems into cloud-native architectures while ensuring data integrity, performance, and reliability.

Key Responsibilities

ETL Modernization & Development

• Analyze and migrate legacy ETL workflows from Ab Initio to PySpark-based pipelines
• Design and develop scalable data pipelines on Databricks
• Refactor monolithic processes into modular, reusable components
• Leverage existing enterprise datasets to avoid redundancy

Data Integration & Processing

• Build and maintain ETL/ELT pipelines integrating data from Snowflake and other sources
• Process and publish enriched datasets for downstream applications
• Support batch and near real-time data processing

Data Lineage & Optimization

• Create end-to-end data lineage and data flow diagrams
• Identify redundancies and drive process consolidation and optimization
• Ensure adherence to data governance and quality standards

Testing & Validation

• Develop unit, integration, and reconciliation frameworks
• Perform dual-run comparisons with legacy systems
• Validate outputs in UAT and pre-production environments

Deployment & Operations

• Support cutover and migration strategy from legacy systems
• Decommission legacy workflows and optimize scheduling (e.g., Control-M)
• Develop runbooks, monitoring, and operational documentation

Collaboration

• Work with data architects, analysts, and downstream application teams
• Coordinate user acceptance testing (UAT/FAT) and stakeholder sign-offs