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Leads a cross-functional engineering team responsible for development, modernization, and support of mission-critical mainframe systems integrated with modern platforms (ZConnect, ZDIH) and enterprise DevOps tooling (GitLab CI/CD).

This role drives end-to-end ownership across architecture, engineering delivery, QA practices, and DevOps pipelines, ensuring scalable, secure, and high-quality solutions aligned with enterprise SDLC and regulatory standards.

Key Responsibilities

1. Technical Leadership & Delivery Ownership

• Lead design, development, and support of mainframe applications (z/OS, COBOL, JCL, DB2, VSAM) integrated with modern services
• Own end-to-end SDLC delivery: design → build → test → deploy → operate
• Serve as technical authority and SME across mainframe and modernization initiatives

2. Solution Architecture & Design Governance

• Define and enforce end-to-end solution architecture across:
• Mainframe systems
• ZConnect API layer
• ZDIH / data integration platforms
• Distributed/cloud ecosystems
• Own creation and governance of:
• Solution Architecture Documents (SAD)
• Component, integration, and data flow diagrams
• Ensure:  
• Loose coupling between legacy and modern systems
• API-first and event-driven design where applicable
• Scalability, resiliency, and performance considerations
• Conduct architecture reviews and design approvals before development initiation

3. DevOps, GitLab & Pipeline Ownership

• Lead adoption and governance of GitLab-based CI/CD pipelines
• Define and enforce:
• Branching strategies (feature / release / hotfix)
• Merge request standards and approvals
• Code quality gates (Sonar, coverage thresholds)
• Drive pipeline standardization and automation, reducing manual steps
• Own pipeline troubleshooting and continuous improvement

4. Modernization & Integration (ZConnect / ZDIH)

• Lead delivery of z/OS Connect APIs enabling mainframe services
• Drive adoption of ZDIH and hybrid integration patterns
• Identify opportunities to:
• Decouple legacy logic
• Enable reusable APIs and services
• Ensure secure and scalable integration with distributed platforms

5. QA Engineering & Quality Practices

Test Strategy & Governance

• Define and enforce end-to-end QA strategy, including:
• Unit testing
• Integration testing
• System / end-to-end testing
• Regression testing
• Ensure test strategy is documented and approved prior to development

Automation & DevOps Integration

• Drive test automation integrated into GitLab pipelines:
• Automated unit tests
• Regression test suites
• Pipeline-based validation and quality gates
• Enforce shift-left testing practices across the team

Quality Engineering Standards

• Establish and enforce:
• Code quality standards (static analysis, peer review)
• Test coverage thresholds
• Defect lifecycle discipline and root cause analysis
• Ensure test data strategy and environment stability

Release & Production Quality

• Define release validation criteria:  
• Pre-production validation
• Deployment smoke tests
• Post-deployment verification
• Reduce:  
• Production defects
• Rework and regression failures

6. Engineering Excellence & Automation

• Drive automation-first mindset:  
• Minimize manual batch/process work
• Automate validation, deployments, and monitoring
• Improve:  
• Cycle time
• Deployment consistency
• Operational resilience

7. Team Leadership & Talent Development

• Lead and mentor engineers across:
• Mainframe technologies
• DevOps practices
• Modern integration stacks
• Build culture of:
• Accountability and ownership
• Peer review and quality discipline
• Continuous learning and upskilling

8. Stakeholder & Delivery Management

• Translate business requirements into scalable technical solutions
• Manage:  
• Backlog prioritization
• Sprint execution
• Delivery commitments
• Ensure adherence to:
• SDLC controls
• Audit and compliance requirements

9. Strategic Leadership & Technology Direction (NEW)

• Define and drive technology strategy for mainframe modernization and hybrid architecture (Mainframe + ZConnect + ZDIH + DevOps)
• Align engineering roadmap with business priorities, cost optimization, and risk reduction goals
• Identify and execute opportunities to:
• Reduce technical debt
• Simplify legacy processes
• Improve platform scalability and resiliency
• Lead transformation initiatives from:
• Batch/report-driven systems → API-driven and data-integrated platforms
• Influence cross-team and enterprise decisions on standards, tooling, and architecture

10. Metrics-Driven Engineering & Execution Discipline (NEW)

• Establish and enforce a metrics-driven operating model across engineering and delivery
• Define, track, and report on key performance indicators (KPIs), including:
• Sprint predictability (≥90% commitment adherence)
• Deployment frequency and lead time
• Defect density and production leakage
• Test automation coverage
• Pipeline success/failure rates
• Drive data-based decision making for:
• Capacity planning
• Prioritization and backlog management