Lead Software Engineer - Data and Payments Business Observability Platform

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer - Data and Payments Business Observability Platform at JPMorgan Chase within the Commercial and Investment Banking - Data Analytics Payment Team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Lead the design and delivery of an activity monitoring solution spanning a React UI and Java/Spring Boot backend services, ensuring scalability, resiliency, and a low-latency user experience with responsive APIs.

• Lead the design and evolution of event-driven microservices workflows, using Kafka for inter-service communication and reliable asynchronous processing (at-least-once delivery, retries, and DLQ patterns).

• Set and uphold engineering standards for the team’s Spring Boot services (API design, error handling, security, logging, performance tuning, versioning).

• Partner with product owners and stakeholders to translate monitoring/operational requirements into clear technical designs, delivery plans, and measurable outcomes.

• Design and maintain data access patterns across MySQL and Databricks, including connectivity, data retrieval strategies, and performance-efficient query patterns.

• Establish and evolve the deployment strategy (blue/green, canary, rollback, configuration management) aligned to engineering and operational needs on AWS.

• Own and improve CI/CD pipelines using Jenkins, including automated builds, tests, quality gates, release orchestration, and environment promotions.

• Design, build, and maintain the React-based UI, including performant, data-heavy grid experiences using AG Grid, state management, and frontend quality controls.

• Ensure production readiness through observability (metrics, logs, tracing), alerting, incident response runbooks, and proactive problem management.

• Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns.

• Apply knowledge of SDLC toolchains, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.