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Job Description

We are seeking a highly experienced Senior ETL Developer with deep expertise in IBM DataStage, PySpark, and Mainframe modernization to support a large-scale enterprise data transformation initiative. This hands-on role is responsible for designing, developing, modernizing, and optimizing enterprise ETL solutions while transforming legacy mainframe batch processes into scalable, high-performance data pipelines.

The ideal candidate will have strong experience developing enterprise ETL workflows using IBM DataStage, translating legacy COBOL, JCL, and VSAM logic into modern data integration solutions, and building scalable PySpark-based data processing pipelines. You will collaborate with business stakeholders, architects, and engineering teams to deliver secure, reliable, and high-quality data solutions that support enterprise modernization initiatives across AWS and Snowflake environments.

Key Responsibilities

• Design, develop, enhance, and maintain enterprise-scale ETL solutions using IBM DataStage.
• Modernize legacy mainframe batch applications by translating COBOL, JCL, and VSAM business logic into scalable DataStage ETL workflows.
• Develop and optimize PySpark applications for large-scale data transformation, processing, and migration initiatives.
• Design robust ETL workflows for data extraction, transformation, integration, and loading across multiple enterprise systems.
• Build scalable and high-performance data pipelines supporting cloud migration and modernization initiatives.
• Collaborate with business analysts, solution architects, data engineers, and application teams to understand business requirements and implement effective ETL solutions.
• Optimize ETL job performance through efficient DataStage design, SQL tuning, partitioning, and parallel processing techniques.
• Support migration of enterprise data assets to modern cloud platforms including Snowflake and AWS.
• Perform requirements analysis, development, unit testing, integration testing, deployment, and production support throughout the Software Development Life Cycle (SDLC).
• Troubleshoot production issues, perform root cause analysis, and implement long-term solutions to improve platform stability and reliability.
• Ensure data quality, integrity, governance, and operational excellence across enterprise ETL processes.
• Create and maintain technical documentation, ETL design specifications, implementation guides, and operational runbooks.
• Participate in code reviews and promote ETL development standards, best practices, and continuous improvement initiatives.

Required Qualifications

• 6+ years of hands-on experience developing enterprise ETL solutions using IBM DataStage.
• Strong expertise in designing, developing, and maintaining complex enterprise ETL workflows and data integration solutions.
• Hands-on experience with PySpark for large-scale data processing and transformation.
• Proven experience modernizing legacy Mainframe applications, including COBOL, JCL, VSAM
• Strong understanding of batch processing frameworks and enterprise data integration architecture.
• Experience working with modern cloud data platforms including AWS and Snowflake.
• Strong SQL development skills, including Complex joins, Stored procedures, Performance tuning, Query optimization
• Experience throughout the full Software Development Life Cycle (SDLC), including requirements gathering, development, testing, deployment, and production support.
• Experience troubleshooting ETL jobs and resolving complex production issues.
• Strong understanding of data warehousing concepts, ETL best practices, and enterprise data integration.
• Excellent analytical, problem-solving, communication, and stakeholder management skills.
• Ability to collaborate effectively with cross-functional business and technology teams in a client-facing consulting environment.

Preferred Qualifications

• Experience migrating enterprise ETL workloads to cloud platforms such as AWS and Snowflake.
• Experience supporting enterprise modernization and legacy system transformation programs.
• Knowledge of performance optimization techniques for large-scale DataStage and PySpark workloads.
• Experience with CI/CD pipelines, DevOps practices, and automated deployment frameworks.
• Familiarity with enterprise scheduling tools such as Autosys, Control-M, or similar workload automation platforms.
• Experience working in Financial Services, Banking, or other highly regulated industries.
• Exposure to Agile/Scrum software development methodologies.