Sr Manager of Software Engineering - Supply Chain

JPMorganChase•Houston, TX

10d

About The Position

This is your chance to change the path of your career and guide multiple teams to success at one of the world's leading financial institutions. As a Manager of Software Engineering at JPMorganChase within the Corporate Sector - Infrastructure Platform Supply Chain platform, you lead multiple teams and manage day-to-day implementation activities by identifying and escalating issues and ensuring your team’s work adheres to compliance standards, business requirements, and tactical best practices.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience.
Demonstrated coaching and mentoring experience
Experience managing, mentoring, and developing software engineering teams in a large corporate or financial services environment
Demonstrated experience leading software delivery across architecture, application development, testing, deployment, release management, and operational stability
Strong understanding of software engineering strategy, technical roadmaps, delivery governance, and organizational change
Experience leading agile teams and managing program increments, delivery dependencies, backlogs, and continuous improvement
Deep understanding of cloud architecture, virtualization, APIs, data models, event-driven solutions, and modern software frameworks
Strong knowledge of SDLC practices, CI/CD, DevSecOps, application resiliency, security, and automated testing
Experience establishing or improving engineering standards, release processes, operational controls, and production readiness practices
Understanding of technology risk management, governance, regulatory expectations, and control processes
Experience leading responsible adoption of enterprise-authorized AI-assisted development and delivery tools across engineering teams, including defining ways of working (review/validation expectations), measuring outcomes, and ensuring secure handling of data.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and governance

Nice To Haves

Familiarity with modern front-end technologies
Exposure to cloud technologies and cloud-native architecture
Experience with enterprise architecture, platform engineering, or large-scale distributed systems
Experience with release orchestration, DevSecOps tooling, observability, and automated deployment platforms
Experience managing technology risk, audit activities, controls, or regulatory engagements
Experience leading engineering organizations through large-scale transformation or modernization initiatives

Responsibilities

Leads, develops, and manages software engineering teams, fostering a culture of inclusion, accountability, technical excellence, continuous improvement, and collaboration
Establishes and executes engineering strategy, roadmaps, priorities, and delivery plans aligned with business objectives
Oversees software solution design, development, testing, deployment, and technical troubleshooting across multiple applications and technical domains
Partners with product and business stakeholders to define priorities, manage scope, resolve dependencies, and ensure predictable delivery
Oversees release management, deployment coordination, production readiness, change management, and operational stability
Drives adoption and continuous improvement of CI/CD pipelines, automated testing, infrastructure automation, observability, and secure software delivery practices
Identifies, assesses, and mitigates technology, delivery, operational, cyber, and regulatory risks
Ensures the creation and maintenance of architecture, design, risk, control, and delivery artifacts
Uses data, metrics, and reporting to identify trends, improve engineering performance, and strengthen application and system health
Leads team adoption of enterprise-authorized AI-assisted engineering practices and SDLC/TLM automation to improve delivery speed, quality, and operational outcomes, while setting expectations for human validation, secure handling of inputs/outputs, and consistent use of reusable patterns across teams.