Lead Software Engineer - Observability Event Java Full Stack

As a Lead Software Engineer at JPMorgan

Chase, you will be at the center of a high-impact modernization effort, leading the evolution of firmwide alerting and event-routing infrastructure from a legacy monolith to a modern, cloud-native microservices platform. You will partner across engineering, site reliability, product, and controls teams to deliver resilient, scalable systems — including a new AI-powered monitoring tool that brings intelligent event enrichment, correlation, and root cause analysis to the firm.

This is a brilliant role for an engineer with a full stack (but backend-dominant) Java skillset; who brings deep technical expertise to raise the bar for the teams around them.

• Execute creative software solutions across design, development, and advanced technical troubleshooting, think beyond routine approaches to build solutions and break down complex technical problems.
• Lead the migration from a legacy monolith to a modern, containerized microservices architecture (Java/Spring Boot; UI modernization as needed), including decomposition strategy, domain modeling, and data migration planning.
• Re‑architect existing infrastructure to achieve high scalability, reliability, and availability (multi‑AZ/region patterns, autoscaling, HA/DR).
• Deliver features end‑to‑end, build backend services and APIs (REST/GraphQL) in Java/Spring Boot and contribute to operational UIs/console experiences as needed.
• Design and implement event‑driven systems and streaming‑based alerting workflows (e.g., Kafka), including sound topic/schema design and resilient consumer strategies.
• Build and evolve secure, high‑quality production services; review, debug, and improve code written by others to raise engineering standards.
• Drive performance, resiliency, and scalability improvements for firmwide alerting and event routing; instrument SLOs/SLIs and optimize p99 latency and throughput. Collaborate cross‑LOB with product, application teams, SRE/operations, and controls to ensure reliable adoption and effective incident response.
• Explore and evaluate AI for Ops approaches with partners across SRE and observability. Drive POCs and data‑driven adoption where appropriate.
• Lead evaluation and architecture discussions with internal teams (and vendors when applicable) to assess designs, technical credentials, and fit within enterprise architectures. Contribute to communities of practice to promote awareness and adoption of modern engineering and observability practices.
• Add to a team culture of diversity, opportunity, inclusion, and respect.
• Drives team adoption of enterprise‑authorized AI‑assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI‑assisted code review/refactoring, test strategy acceleration, incident/root‑cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise‑authorized AI‑assisted development and automation capabilities, to improve the value realized by automation.

• Significant professional software engineering experience, including leading cross‑team initiatives (Lead level).
• Hands‑on experience delivering system design, application development, testing, and operational stability for distributed systems.
• Advanced proficiency in Java and building microservices with Spring Boot, strong system design expertise.
• Backend‑focused engineering mindset with the ability to contribute across layers as needed, including partnering effectively on UI work…