Engineering Manager - Cloud Platform Engineering

Location: St James's

hackajob is partnering directly with JPMorgan

Chase to hire for this role.

JOB DESCRIPTION Join us as we build products that solve real-world problems and put customers at the center of everything we do. You'll be part of a team that values collaboration, curiosity, and commitment, helping you realize your potential in an environment that nurtures skills and growth. We're people-first, and your leadership will be key to our success. At JPMorgan Chase, you'll have the opportunity to impact your career and help shape the future of our platform.

Discover a place where your ideas matter and your growth is supported.

Job Summary:

As an Engineering Manager (Platform) in the Accelerator Business Platform Team, you will lead a high-performing squad focused on delivering scalable solutions that bring smart ideas to our customers. You will bridge the gap between technical strategy and business goals, cultivate engineering talent, and foster a culture of diversity, equity, inclusion, and respect. You will be responsible for the growth, health, and delivery of your engineering team, ensuring products are built to scale and aligned with our mission.

Your role will empower you to drive innovation and help shape the team's impact.

Job Responsibilities:

Provide direction, oversight, and coaching for a team of software engineers, fostering an inclusive, high-performance culture. Oversee end-to-end delivery of platform initiatives, translating requirements into technical roadmaps and ensuring operational stability. Drive engineering standards and best practices, promoting high-quality code, robust release processes, and scalable infrastructure. Partner with senior leadership to set the long-term technical vision for the platform, aligning with business outcomes and regulatory requirements.

Champion a "you-build-it-you-run-it" culture, overseeing incident response and identifying opportunities to automate remediation. Ensure successful collaboration across teams and stakeholders, driving consensus on architectural and delivery trade-offs. Identify and mitigate issues to execute a book of work, contributing to communities of practice and adoption of leading-edge technologies. Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes;

establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns. Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets.

Required Qualifications , Capabilities, and

Skills:

Formal training or certification in software engineering concepts. Experience leading technologists to manage and solve complex technical challenges. Proven people-management or team-lead experience, with a track record of coaching engineers and driving team performance. Strong background in software engineering, including system design, cloud native systems, and Kubernetes. Ability to guide technical decisions and evaluate designs. Experience managing complex technical delivery in an agile environment.

Exceptional communication skills for influencing technical and non-technical stakeholders. Ability to translate business objectives into team-level tasks and priorities. Experience managing production systems, including on-call rotations, incident response, and reliability engineering. Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.

Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach…