Lead Software Engineer - Fullstack Java

JPMorganChase•Columbus, OH

About The Position

As a Senior Lead Software Engineer within JPMorganChase’s Community and Consumer Banking - Business Banking Technology organization, 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. The role leads full-stack engineering across Java and Spring Boot services and React or Angular user experiences. It includes driving Kafka-based event-driven architecture, strengthening engineering and production practices, improving observability, and mentoring engineers. You will also drive governance and adoption of approved AI-assisted engineering practices, establish measurable validation standards, and promote reusable automation and patterns across the software development lifecycle toolchain.

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Advanced hands-on experience building microservices and APIs using Java 8 or later and Spring Boot in large-scale environments.
Strong full-stack engineering depth with modern web frameworks such as React or Angular, and pragmatic API/UI design tradeoffs.
Proven experience building and operating event-driven systems with Kafka, including retries, idempotency, ordering, and dead-letter queues.
Solid SQL database skills, including data modeling, query optimization, and performance tuning.
Experience with NoSQL data stores such as 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 such as authentication and authorization, secure APIs, data protection, and dependency hygiene.
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools in the work environment, such as for coding, code review, test acceleration, and troubleshooting; 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, secure handling of inputs and outputs, and adherence to resiliency and security expectations.
Experience coaching senior engineers or leads on compliant AI usage patterns and controls.

Nice To Haves

Experience building client-facing digital products in banking, lending, payments, or small-business platforms.
Experience deploying and operating services on cloud platforms such as 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 PCI DSS requirements and secure-by-design engineering practices.
Relevant cloud or security certifications aligned to the role’s focus areas.

Responsibilities

Lead technical design and delivery of complex full-stack capabilities across services and user experiences using 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, CI/CD quality gates, and release readiness.
Own production excellence by reducing incidents, improving root-cause analysis and 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 with product, operations, and engineering teams to align delivery plans, manage technical risk, and deliver measurable outcomes.
Drive adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes.
Establish measurable validation standards for AI-assisted engineering, including secure coding, peer review, and automated testing; promote reuse of proven patterns and automation within the SDLC/TLM toolchain.
Apply knowledge of software development lifecycle tools, including approved AI-assisted development and automation capabilities, to improve the value realized through automation at scale.