Lead Software Engineer; Java​/Python - Enterprise Technology Data Protection & Recovery

Job Description

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.

• 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.
• 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.
• Build and orchestrate Agentic AI capabilities that automate and execute complex actions within enterprise SAP processes.
• 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.
• Enhance existing systems by analyzing objectives, preparing action plans, and identifying opportunities for performance, reliability, and operability improvements.
• Help support CI/CD quality within the product line, partnering with the engineering and product teams: codify automated tests (unit, integration, contract, performance), coverage targets, pipeline quality gates, and progressive delivery (canary/blue-green) with rollback plans.
• Contribute to the engineering community as an advocate of firmwide frameworks, tools, and best practices; add to team culture by fostering diversity, inclusion, and respect.
• Support critical environments (Development, QA, Simulation, Production) and lead on-call obligations for owned services, ensuring operational stability and timely resolution of incidents.

• Formal training or certification on software engineering concepts and 5+ years applied experience
• 8+ of experience with an expert level Java proficiency, including collections, concurrency, memory management, and performance tuning; experience with profiling tools. Able to walk through applied examples designing and operating distributed Java systems.
• Strong experience with Spring Boot and microservices; API focused event driven design and idempotency; schema evolution and compatibility.
• Expert-level experiencing designing and implementing Python-based microservices.
• Deep knowledge of resiliency engineering patterns and disaster recovery objectives (RTO/RPO) with hands on practice running DR tests and chaos/resilience exercises.
• Secure SDLC expertise: threat modeling, SAST/DAST integration, dependency risk/SBOM management, secrets handling, encryption standards, and robust authentication/authorization.
• Streaming and messaging: practical experience with Kafka (topic design, partitioning, consumer group scaling, transactional/exactly-once semantics) and other messaging platforms as required.
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
• Observability: instrumentation of metrics/logs/traces, alerting design, runbooks, incident…