Senior Mainframe & DB2 Migration Data Engineer

Senior Mainframe DB2 Migration Data Engineer (8-10 Years Experience) Job Title

Senior Mainframe DB2 Migration Data Engineer

Experience

8-10 Years

Job Summary

We are seeking an experienced Senior Mainframe DB2 Migration Data Engineer with 8-10 years of expertise in mainframe data engineering, legacy modernization, and cloud data platforms. The ideal candidate will play a key role in a large-scale enterprise modernization initiative, migrating legacy applications and data from COBOL, EasyTreive, JCL, DFSORT, DB2, VSAM, and GDG environments to a modern Databricks platform.

The role requires deep expertise in analyzing mainframe data structures, designing scalable data pipelines, performing EBCDIC data conversion, implementing business-rule transformations, and ensuring seamless migration with minimal business disruption. The successful candidate will collaborate with architects, application teams, business analysts, and QA teams to deliver high-quality migration outcomes through structured delivery waves.

Project Overview

The modernization program includes:

• Three IM application areas running on COBOL / DB2 / VSAM / GDG
• Source data consisting of:
  • GDG files
  • Fixed-width flat files
  • DB2 databases
• Migration of:
  • COBOL logic
  • EasyTrieve
  • JCL orchestration
  • Copybook schemas
  • Business rules
  • Validation processes
• Execution through structured migration waves.

Success will be measured by:

• Output parity with legacy systems
• SLA compliance
• Accurate mainframe data conversion
• Validated business-rule equivalence
• High-performance and reliable data pipelines on Databricks

Key Responsibilities Mainframe Data Migration

• Analyze COBOL programs, DB2 databases, VSAM datasets, GDG files, JCL workflows, and copybook definitions to understand legacy processing and data dependencies.
• Design and implement migration strategies for mainframe data assets into modern cloud-based data platforms.
• Develop automated ingestion processes for GDG, VSAM, DB2, and fixed-width flat files.
• Convert EBCDIC-encoded datasets into standardized modern data formats while preserving data quality and integrity.
• Support phased migration delivery across multiple application domains with minimal operational impact.

Data Engineering Platform Development

• Design, build, and optimize scalable ETL/ELT pipelines using Databricks, PySpark, Spark SQL, and Delta Lake.
• Develop Bronze, Silver, and Gold data layers following modern lakehouse architecture principles.
• Implement reusable ingestion and transformation frameworks.
• Optimize Spark workloads for performance, scalability, and cost efficiency.
• Implement monitoring, logging, metadata management, and data lineage.

Business Logic Data Transformation

• Translate COBOL business rules into modern data transformation logic.
• Work closely with business analysts and application SMEs to validate functional equivalence.
• Support implementation of transformation rules that preserve existing business processes.

Data Validation Quality Assurance

• Perform source-to-target mapping and detailed data reconciliation.
• Develop automated validation frameworks to compare legacy and target outputs.
• Ensure output parity between mainframe and Databricks environments.
• Resolve data quality issues and support defect remediation during migration testing.

Performance Operational Excellence

• Ensure migration deliverables meet defined SLAs and operational requirements.
• Monitor pipeline execution, optimize processing times, and troubleshoot production issues.
• Participate in migration cutover planning and production deployment activities.
• Contribute to migration standards, reusable assets, and best practices.

Collaboration Leadership

• Collaborate with solution architects, developers, testers, infrastructure teams, and business stakeholders.
• Mentor junior data engineers and provide technical guidance on migration activities.
• Participate in design reviews, code reviews, and technical discussions.
• Support Agile ceremonies including sprint planning, estimation, and retrospectives.

Required Technical Skills Mainframe Technologies

• COBOL
• DB2
• VSAM
• GDG (Generation Data Groups)
• JCL
• Copybook processing
• Fixed-width file handling
• EBCDIC to ASCII/UTF-8 conversion
• Mainframe batch processing

Modern Data Engineering

• Databricks
• Apache Spark
• PySpark
• Spark SQL
• Delta Lake
• Data Lakehouse architecture
• ETL/ELT pipeline development
• Workflow orchestration

Databases Data Management

• Advanced SQL
• Data modeling
• Source-to-target mapping
• Data reconciliation
• Data quality frameworks
• Metadata management
• Data lineage

Cloud Technologies

Experience with one or more cloud platforms:

• Microsoft Azure (Preferred)
  • Azure Databricks
  • Azure Data Lake Storage (ADLS)
  • Azure Data Factory (ADF)
• AWS or Google Cloud Platform (desirable)

Development DevOps

• Python
• Shell scripting
• Git
• CI/CD pipelines
• Azure DevOps or Jenkins

Required Experience

• 8-10 years of experience in Data Engineering, Mainframe Development, or Mainframe Modernization.
• Minimum 5 years of hands-on experience with COBOL, DB2, VSAM, GDG, and JCL.
• Minimum 3 years of experience building scalable data pipelines using Databricks, PySpark, and Spark SQL.
• Experience migrating enterprise mainframe applications to cloud-based data platforms.
• Strong understanding of copybook parsing, EBCDIC conversion, and legacy file structures.
• Proven experience in source-to-target mapping, reconciliation, and migration validation.
• Experience working in Agile development environments.
• Strong analytical, troubleshooting, and communication skills.

Preferred Qualifications

• Experience in enterprise-scale mainframe modernization initiatives.
• Knowledge of financial services, insurance, healthcare, or government legacy systems.
• Experience with automated testing and reconciliation frameworks.
• Understanding of Data Governance, Data Quality, and Master Data Management.
• Databricks, Azure, or cloud certification is a plus.

Senior Mainframe DB2 Migration Data Engineer (8-10 Years Experience) Job Title

Senior Mainframe DB2 Migration Data Engineer

Experience

8-10 Years

Job Summary

We are seeking an experienced Senior Mainframe DB2 Migration Data Engineer with 8-10 years of expertise in mainframe data engineering, legacy modernization, and cloud data platforms. The ideal candidate will play a key role in a large-scale enterprise modernization initiative, migrating legacy applications and data from COBOL, DB2, VSAM, and GDG environments to a modern Databricks platform.

The role requires deep expertise in analyzing mainframe data structures, designing scalable data pipelines, performing EBCDIC data conversion, implementing business-rule transformations, and ensuring seamless migration with minimal business disruption. The successful candidate will collaborate with architects, application teams, business analysts, and QA teams to deliver high-quality migration outcomes through structured delivery waves.

Project Overview

The modernization program includes:

• Three IM application areas running on COBOL / DB2 / VSAM / GDG
• Source data consisting of:
  • GDG files
  • Fixed-width flat files
  • DB2 databases
• Migration of:
  • COBOL logic
  • JCL orchestration
  • Copybook schemas
  • Business rules
  • Validation processes
• Execution through structured migration waves.

Success will be measured by:

• Output parity with legacy systems
• SLA compliance
• Accurate mainframe data conversion
• Validated business-rule equivalence
• High-performance and reliable data pipelines on Databricks

Key Responsibilities Mainframe Data Migration

• Analyze COBOL programs, DB2 databases, VSAM datasets, GDG files, JCL workflows, and copybook definitions to understand legacy processing and data dependencies.
• Design and implement migration strategies for mainframe data assets into modern cloud-based data platforms.
• Develop automated ingestion processes for GDG, VSAM, DB2, and fixed-width flat files.
• Convert EBCDIC-encoded datasets into standardized modern data formats while preserving data quality and integrity.
• Support phased migration delivery across multiple application domains with minimal operational impact.

Data Engineering Platform Development

• Design, build, and optimize scalable ETL/ELT pipelines using Databricks, PySpark, Spark SQL, and Delta Lake.
• Develop Bronze, Silver, and Gold data layers following modern lakehouse architecture principles.
• Implement reusable ingestion and transformation frameworks.
• Optimize Spark workloads for performance, scalability, and cost efficiency.
• Implement monitoring, logging, metadata management, and data lineage.

Business Logic Data Transformation

• Translate COBOL business rules into modern data transformation logic.
• Work closely with business analysts and application SMEs to validate functional equivalence.
• Support implementation of transformation rules that preserve existing business processes.

Data Validation Quality Assurance

• Perform source-to-target mapping and detailed data reconciliation.
• Develop automated validation frameworks to compare legacy and target outputs.
• Ensure output parity between mainframe and Databricks environments.
• Resolve data quality issues and support defect remediation during migration testing.

Performance Operational Excellence

• Ensure migration deliverables meet defined SLAs and operational requirements.
• Monitor pipeline execution, optimize processing times, and troubleshoot production issues.
• Participate in migration cutover planning and production deployment activities.
• Contribute to migration standards, reusable assets, and best practices.

Collaboration Leadership

• Collaborate with solution architects, developers, testers, infrastructure teams, and business stakeholders.
• Mentor junior data engineers and provide technical guidance on migration activities.
• Participate in design reviews, code reviews, and technical discussions.
• Support Agile ceremonies including sprint planning, estimation, and retrospectives.

Required Technical Skills Mainframe Technologies

• COBOL
• DB2
• VSAM
• GDG (Generation Data Groups)
• JCL
• Copybook processing
• Fixed-width file handling
• EBCDIC to ASCII/UTF-8 conversion
• Mainframe batch processing

Modern Data Engineering

• Databricks
• Apache Spark
• PySpark
• Spark SQL
• Delta Lake
• Data Lakehouse architecture
• ETL/ELT pipeline development
• Workflow orchestration

Databases Data Management

• Advanced SQL
• Data modeling
• Source-to-target mapping
• Data reconciliation
• Data quality frameworks
• Metadata management
• Data lineage

Cloud Technologies

Experience with one or more cloud platforms:

• Microsoft Azure (Preferred)
  • Azure Databricks
  • Azure Data Lake Storage (ADLS)
  • Azure Data Factory (ADF)
• AWS or Google Cloud Platform (desirable)

Development DevOps

• Python
• Shell scripting
• Git
• CI/CD pipelines
• Azure DevOps or Jenkins

Required Experience

• 8-10 years of experience in Data Engineering, Mainframe Development, or Mainframe Modernization.
• Minimum 5 years of hands-on experience with COBOL, DB2, VSAM, GDG, and JCL.
• Minimum 3 years of experience building scalable data pipelines using Databricks, PySpark, and Spark SQL.
• Experience migrating enterprise mainframe applications to cloud-based data platforms.
• Strong understanding of copybook parsing, EBCDIC conversion, and legacy file structures.
• Proven experience in source-to-target mapping, reconciliation, and migration validation.
• Experience working in Agile development environments.
• Strong analytical, troubleshooting, and communication skills.

Preferred Qualifications

• Experience in enterprise-scale mainframe modernization initiatives.
• Knowledge of financial services, insurance, healthcare, or government legacy systems.
• Experience with automated testing and reconciliation frameworks.
• Understanding of Data Governance, Data Quality, and Master Data Management.
• Databricks, Azure, or cloud certification is a plus.

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