As a Lead Infrastructure Engineer at JPMorganChase within the [insert LOB or sub LOB], you apply deep knowledge of software, applications, and technical processes within the infrastructure engineering discipline. Continue to evolve your technical and cross-functional knowledge outside of your aligned domain of expertise.
Job responsibilities
- Engineer and govern the Endevor inventory model (environments/systems/subsystems/types/stages/mappings) aligned to SDLC, release governance, and separation of duties.
- Configure and maintain processors / generate procedures to enable deterministic builds (compile/link/edit flows, copybook/include handling, and middleware hooks such as DB2/CICS/IMS where applicable).
- Build and maintain Endevor automation interfaces using supported APIs/CLI plus custom wrapper services/scripts, including versioning, backward compatibility, error contracts, and usage standards.
- Develop Python automation and wrapper tooling to orchestrate Endevor lifecycle actions (create/update packages, move/generate/promote), capture metadata/evidence, integrate with approvals, and publish results to downstream systems. Develop REXX utilities for z/OS-side automation (inventory queries, dataset/member validation, job/JCL generation, operational tooling, exception reporting), integrating with wrapper interfaces where needed.
- Implement and support Jenkins pipelines (pipeline-as-code) that invoke Endevor via API/CLI wrappers, manage credentials securely, submit/monitor batch steps when required, and enforce promotion sequencing.
- Implement traceability and provenance end-to-end: map commit/build IDs to Endevor element/package identifiers, capture promotion history, label builds/releases, and support audit evidence retrieval.
- Troubleshoot complex failures across the toolchain (processor failures, package conflicts, wrapper/API errors, Jenkins pipeline breaks, inventory drift), delivering RCA and implementing preventive fixes.
- Define, test, and execute backup/restore and DR procedures for Endevor repositories, critical datasets, and wrapper components, including routine DR exercises and validation evidence.
- Uses enterprise-authorized AI capabilities within the work environment to accelerate infrastructure analysis and design documentation, validating outputs and handling operational data according to sensitivity and security requirements.
- Drives a workstream or project consisting of one or more infrastructure engineering technologies and works with other platforms to architect and implement changes required to resolve issues and modernize the organization and technology processes
- Applies reuse-first, AI-assisted practices within delivery and automation routines to identify recurring issues and validate remediation options, ensuring changes are traceable/auditable and aligned to resiliency and security expectations.
- Formal training or certification on infrastructure engineering concepts and 5+ years applied experience
- Endevor platform engineering: configure/operate CA-Endevor (inventory models, processors/generates, packages/promotion rules), perform upgrades/maintenance, and ensure stability/DR readiness.
- CI/CD delivery with Jenkins: build pipeline-as-code and shared libraries to automate build/test/promote flows, enforce sequencing, approvals, and environment-specific controls.
- API/CLI + custom wrapper enablement: develop and support versioned wrapper interfaces around Endevor APIs/CLI with consistent error handling, logging, and operational supportability.
- REXX & Python automation: create REXX utilities for z/OS-side operational automation and Python services/scripts for orchestration, integrations, evidence/metadata capture, and notifications.
- Security, traceability, and reliability: implement least-privilege access and audit logging (RACF/ACF2/TSS + Endevor), ensure end-to-end traceability (commit→build→package), and lead troubleshooting/RCA across Endevor/Jenkins/wrappers.
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support infrastructure engineering workflows with strong validation habits and awareness of data sensitivity.
- Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations.
- Deep knowledge of cloud infrastructure and multiple cloud technologies with the ability to operate in and migrate across public and private clouds