Sr. Manager of Software Engineering- Business Banking

JPMorganChase•Columbus, OH

About The Position

Join JPMorganChase’s Business Banking Technology team to build secure, stable, and scalable digital solutions that help small businesses manage their finances and achieve their goals. The role shapes technical direction, modernizes core capabilities, and advances engineering excellence across a growing organization. As a Senior Manager of Software Engineering within Community and Consumer Banking - Business Banking Technology, you will own complex services and end-to-end delivery, influence architecture across squads, and improve engineering practices to raise quality, reliability, and delivery speed. You will partner with business, product, and operations teams to deliver resilient platforms and strong client experiences. The role also drives responsible adoption and governance of approved AI-assisted engineering tools and practices, with measurable validation standards and reusable automation across the software development lifecycle and 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 experience with React or Angular and pragmatic API/UI design tradeoffs.
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.
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 expectations for validating AI outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and compliance with 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 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, Spring Boot, and React or Angular.
Drive event-driven architecture patterns with 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, preventing root causes, 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, while influencing standards and reusable patterns across squads.
Partner with product, operations, and engineering teams to align delivery plans, manage technical risk, and achieve 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, and promote reuse of proven patterns and automation within the SDLC/TLM toolchain.
Apply software development lifecycle toolchain knowledge, including approved AI-assisted development and automation capabilities, to improve the value realized through automation at scale.