Lead QA Engineer - Mainframe Testing

Job Title: Lead QA Engineer - Mainframe Testing

Location: US (Remote)

Job Type: Contract

Job Description :

Experience: 8–10 years

Tech Stack: Mainframe (COBOL, IMS, DB2, JCL, VSAM, CICS), AWS, Go, Python, AI-Assisted Testing

Role Overview

We are looking for a Senior QA Engineer with hands-on mainframe (MF) knowledge across COBOL, IMS, and DB2 to validate our mainframe-to-AWS modernization program. This role has two parts that carry equal weight: understanding how the legacy platform behaves today well enough to define what "correct" looks like, and building Python and Go automation that proves the modernized AWS workloads produce the same results. You will own test strategy for parallel runs, data migration validation, batch equivalence, and cutover readiness, working closely with mainframe developers, cloud engineers, and architects.

Key Responsibilities

• Define and own the test strategy, plans, and coverage for mainframe modernization workstreams (replatform and refactor), including functional, regression, integration, performance, and cutover testing
• Read and analyze COBOL, JCL, PL/I, and copybooks to derive test conditions, boundary cases, and expected results from business logic embedded in legacy code
• Execute and validate batch and online scenarios on z/OS, including JCL job streams, CICS and IMS DC transactions, and scheduler dependencies in CA-7 or Control-M
• Run parallel tests by feeding identical inputs to the legacy and modernized systems, diffing outputs at field level, and classifying each difference (defect, expected variance, rounding, or encoding)
• Validate data migration from DB2 z/OS, IMS DB, VSAM, and flat files to AWS targets (Aurora/PostgreSQL, DynamoDB, S3, Redshift), including row counts, checksums, field-level reconciliation, and COMP-3 to decimal precision and rounding checks
• Verify batch job equivalence: job sequence, return codes, restart and recovery behavior, and scheduling dependencies, including batch chains replaced by AWS Step Functions, SQS, SNS, and EventBridge
• Build and maintain automated test frameworks in Python (pytest, boto3, pandas) and use Go for high-throughput utilities such as large-file reconciliation and load generation
• Test APIs and new web front ends that replace 3270 screens, using requests/httpx, Postman/Newman, Pact, Playwright, or Selenium
• Plan and run performance, batch window comparison, failover, and rollback rehearsals ahead of each cutover
• Build test data by masking, subsetting, and generating synthetic data from production-like mainframe extracts, and confirm no unmasked PHI or member data reaches non-production AWS environments
• Integrate automated suites into CI/CD (GitHub Actions, Jenkins, AWS CodePipeline/CodeBuild) and provision repeatable test environments with Terraform or CloudFormation, Docker, and ECS/EKS
• Log, triage, and track defects in Jira, and publish results through Allure or pytest-html with clear evidence for release sign-off
• Follow change management, testing, and compliance practices appropriate for business-critical systems
• Mentor QA engineers on mainframe testing concepts and modernization test practices

Required Qualifications

• Mandatory – Mainframe Testing
• Bachelor's degree in computer science, Engineering, or a related field, or equivalent practical experience
• 8–10 years of software testing experience, including at least 4 years testing mainframe applications or mainframe data
• Ability to read COBOL and JCL, with working knowledge of PL/I, copybooks, and EBCDIC vs ASCII handling
• Hands-on experience with DB2 for z/OS (SQL, query validation), IMS DB (segments, hierarchical data), and IMS DC transactions
• Experience with VSAM and fixed-width flat files, including packed decimal (COMP-3) fields
• Experience with CICS, TSO/ISPF, 3270 emulators, and batch schedulers (CA-7 or Control-M)
• Strong Python test automation skills, with pytest as the primary framework and pandas for data comparison
• Working proficiency in Go for building supporting test and reconciliation utilities
• Working knowledge of core AWS services (S3, Lambda, RDS/Aurora, DMS, IAM, CloudWatch) sufficient to test mainframe workloads running on AWS
• Hands-on experience designing parallel run, data migration validation, and batch equivalence tests
• Strong SQL skills across DB2 and PostgreSQL
• Experience with Git, Jira, and CI/CD pipelines
• Strong written and verbal communication to bridge legacy mainframe teams and cloud engineering teams

Technical Skills

• Legacy / Mainframe: COBOL (reading level), JCL, PL/I basics, copybooks, EBCDIC vs ASCII; DB2 z/OS, VSAM, IMS DB, flat files (fixed-width, packed decimal/COMP-3); CICS, TSO/ISPF, IMS DC, CA-7, Control-M; IBM Z Open Editor, Zowe CLI/Zowe Explorer, 3270 emulators (IBM PCOMM, x3270)
• Modernization Test Types: Parallel run and output comparison; data migration validation (row counts, checksums, field-level reconciliation, COMP-3 to decimal precision and rounding); batch job equivalence (sequence, restart/recovery, scheduling dependencies); regression and integration across upstream and downstream systems; performance and cutover testing (batch window comparison, load, failover, rollback rehearsal)
• Python Test Automation: pytest (primary), unittest, Behave or pytest-bdd; boto3, moto, LocalStack; pandas, NumPy, DeepDiff, Great Expectations; cobrix-style layouts via Python struct, ebcdic codecs, copybook parsers, fixedwidth; psycopg2/SQLAlchemy, ibm_db, pyodbc; requests, httpx, Postman/Newman, Pact; Playwright or Selenium; Locust, JMeter, k6
• Go: Test utilities and tooling for high-volume file reconciliation, data validation, and load generation
• AWS Modernization Targets: AWS Mainframe Modernization (Micro Focus/Rocket runtime for replatform, Blu Age for refactor), AWS Transform for mainframe; Lambda, ECS/EKS, EC2; RDS/Aurora (PostgreSQL), DynamoDB, S3, Redshift; AWS DMS, Schema Conversion Tool, DataSync, Transfer Family (SFTP); API Gateway, SQS, SNS, EventBridge, Step Functions; CloudWatch, X-Ray, CloudTrail; IAM, KMS, Secrets Manager, Macie
• Test Data Management: Faker, Delphix, IBM InfoSphere Optim, AWS Glue DataBrew for masking and synthetic data; data subsetting from production-like mainframe extracts
• DevOps and Test Infrastructure: GitHub Actions, Jenkins, AWS CodePipeline/CodeBuild; Terraform or CloudFormation; Docker, EKS, ECS; Git; Jira; Allure, pytest-html
• AI Knowledge & AI-Assisted Testing (Required)
• Practical use of AI coding and assistant tools (e.g., Claude Code, GitHub Copilot, Cursor, or similar) to accelerate COBOL/JCL comprehension, derive test conditions from legacy logic, and draft test cases and automation scripts
• Able to use AI tools to generate candidate test data scenarios, edge cases, and reconciliation checks, and to summarize defect patterns across parallel run results
• Able to critically review and validate AI-generated tests, code, and analysis for correctness before relying on them for release or cutover decisions
• Understanding of secure and compliant AI tool usage, including never entering PHI, member data, or other sensitive information from mainframe systems into prompts or external AI tools
• Able to identify where AI-driven automation can improve test coverage and modernization velocity, and champion adoption within the team

Preferred Qualifications

• Experience in the US health insurance or payer domain, particularly mainframe-based claims processing, eligibility, or core administration systems
• Exposure to healthcare data standards (X12 EDI, HL7, FHIR)
• Prior experience testing mainframe-to-cloud modernization or re-platforming initiatives
• Experience with AWS Mainframe Modernization, AWS Transform, or similar modernization tooling
• Familiarity with mainframe test and debugging tools such as File-AID, Abend-AID, or similar
• Relevant certifications: ISTQB, AWS Certified Cloud Practitioner, AWS Certified Solutions Architect, or IBM mainframe certifications