Lead Software Engineer - Fullstack Java
Posted Updated
Description
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Senior Lead Software Engineer at JPMorganChase within Community and Consumer Banking - Business Banking Technology, you will own complex services and end-to-end delivery, influence architecture across squads, and drive engineering practices that improve quality, reliability, and speed to market. You will partner closely with business, product, and operations teams to deliver resilient platforms and exceptional client experiences.
As a Senior Lead Software Engineer at JPMorganChase within Community and Consumer Banking - Business Banking Technology, you will own complex services and end-to-end delivery, influence architecture across squads, and drive engineering practices that improve quality, reliability, and speed to market. You will partner closely with business, product, and operations teams to deliver resilient platforms and exceptional client experiences.
Job responsibilities
- Lead technical design and delivery of complex full-stack capabilities across services and user experiences (Java and Spring Boot with React or Angular)
- Drive event-driven architecture patterns using Kafka, including idempotency, retries, ordering, schema evolution, and dead-letter queue handling
- Establish and continuously improve engineering practices, including automated testing strategy, continuous integration/continuous delivery (CI/CD) quality gates, and release readiness
- Own production excellence by reducing incidents, improving root cause analysis prevention, tuning performance, and strengthening operational readiness
- Improve observability by defining actionable metrics, dashboards, logs, and alerts using industry-standard monitoring and telemetry tooling
- Mentor engineers through design reviews, pairing, and technical coaching while influencing standards and reusable patterns across squads
- Partner across product, operations, and engineering teams to align delivery plans, manage technical risk, and deliver measurable outcomes
- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Advanced hands-on experience building microservices and APIs using Java (8+) and Spring Boot in large-scale environments
- Strong full-stack engineering depth with modern web frameworks (React or Angular) and pragmatic API/UI design tradeoffs
- Proven experience building and operating event-driven systems with Kafka, including reliability patterns (retries, idempotency, ordering, and dead-letter queues)
- Solid database skills using Structured Query Language (SQL), including data modeling, query optimization, and performance tuning
- Experience with NoSQL data stores (for example Cassandra, DynamoDB, or Elasticsearch), including data modeling for scale and reliability
- Experience designing resilient, highly available architectures, including failure-mode analysis, recovery strategies, and secure engineering practices (authentication/authorization, secure APIs, data protection, dependency hygiene)
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls
Preferred qualifications, capabilities, and skills
- Experience building client-facing digital products in banking, lending, payments, or small-business platforms
- Experience deploying and operating services on cloud platforms such as Amazon Web Services (AWS) and/or Cloud Foundry, including modern delivery pipelines
- Experience with service reliability practices such as service-level objectives, capacity planning, and operational automation
- Familiarity with Payment Card Industry Data Security Standard (PCI DSS) requirements and secure-by-design engineering practices
- Relevant cloud or security certifications aligned to the role’s focus areas