Manager of Software Engineering-Business Banking

JPMorganChase•Columbus, OH

About The Position

Join the Business Banking Technology team to build secure, stable, and scalable digital solutions that help small businesses manage their finances and achieve their goals. The Manager of Software Engineering will shape technical direction, modernize core capabilities, and promote engineering excellence across a growing organization. Within JPMorganChase’s Community and Consumer Banking - Business Banking Technology organization, this role owns complex services and end-to-end delivery, influences architecture across squads, and drives practices that improve quality, reliability, and speed to market. The manager partners with business, product, and operations teams to deliver resilient platforms and strong client experiences. The role leads full-stack engineering across services and user experiences using Java and Spring Boot with React or Angular. It drives Kafka-based event architecture and reliability patterns, improves testing and CI/CD practices, and owns production excellence, performance, observability, and operational readiness. The manager mentors engineers, aligns delivery plans across teams, manages technical risk, and promotes approved AI-assisted engineering practices and automation in the software development lifecycle, with measurable validation for correctness, security, and quality.

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 experience with React or Angular and pragmatic API/UI design tradeoffs.
Proven experience building and operating Kafka-based event-driven systems, including retries, idempotency, ordering, and dead-letter queues.
Solid SQL 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 covering authentication and authorization, secure APIs, data protection, and dependency hygiene.
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools for activities such as coding, code review, test acceleration, and troubleshooting, and setting team expectations for validating 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 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, Spring Boot, and 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 tools.
Mentor engineers through design reviews, pairing, and technical coaching, and influence 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 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, applying software development lifecycle tools and approved AI-assisted development capabilities to improve automation value at scale.