Lead Software Engineer (Java and Python) - Enterprise Technology Data Protection & Recovery

Push the limits of what’s possible with us as an experienced member of our Software Engineering team.

As a Lead Software Engineer at JPMorgan Chase within the Enterprise Technology Data Protection & Recovery product line, you will be an essential member of an agile team dedicated to enhancing, building, and delivering trusted, market leading technology products in a secure, stable, and scalable manner. Your role will involve promoting significant business impact through your skills and contributions, utilizing your deep technical expertise and problem solving methodologies to address a wide range of challenges across technologies and applications. You will collaborate with global product and engineering teams and platform stakeholders to deliver resilient, efficient, and innovative solutions that advance the firm’s strategic objectives.

Job Responsibilities

• Provide hands on technical leadership and guidance across teams; serve as function wide subject matter expert in resiliency and recovery observability.

• Own end to end architecture for critical Java services: define domain boundaries, API contracts, event schemas, and cross-service standards; steward architectural decision records.

• Design, develop, and review secure, high quality production code in Java; debug complex issues; mentor engineers through code reviews and technical coaching.

• 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.

• Drive resilient service design: drive and manage availability SLOs, latency budgets, and error budgets.

• Partner with the engineering lead the secure SDLC: conduct threat modeling; integrate SAST/DAST and dependency risk management; ensure encryption in transit/at rest and robust secrets hygiene; oversee authN/authZ patterns.

• Champion observability and SRE practices: instrument metrics/logs/traces; design actionable alerts; author runbooks; lead incident response, blameless postmortems, and continuous reliability improvements.

• Advance data protection and recovery capabilities: maintain evidence chain of custody and immutable operation, restore validation exercises and auditability.