Technical Lead QA Engineer - Mainframe

Role & 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

Preferred candidate profile

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

• 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