Software Engineer II - Full Stack Engineer

JPMorgan Chase & Co.•Seattle, WA

14h•$118,750 - $150,000

About The Position

You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect software engineering opportunity for you. As a Software Engineer II – Full Stack Engineer at JPMorgan Chase within the Commercial & Investment Bank, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You will also be a hands-on engineer on an Agile team building and operating Spring Boot microservices running on AWS (ECS Fargate or EKS) with Oracle and PostgreSQL (RDS), and a modern web UI built with React. You will design, develop, test, deploy, and support services in a secure, resilient, and observable production environment. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Requirements

Formal training or certification on software engineering concepts and 2+ years applied experience
Proficiency with React (TypeScript, component architecture, state management, performance fundamentals).
Experience with Java 17+ and Spring Boot, including Spring Data JPA / Hibernate (entity modeling, transactions, performance considerations like N+1, pagination).
Hands-on deployment/operations with AWS ECS Fargate or Kubernetes/EKS, and container skills with Docker (image builds, runtime config, troubleshooting).
Practical experience with API Gateway, SQS, SNS, S3, and relational databases on RDS.
Proven experience with Oracle and PostgreSQL (schema design, indexing, query tuning, migrations).
Experience with CI/CD, automated testing (unit/integration), observability (logs/metrics/traces), and application security (authn/authz basics, secrets handling, dependency scanning).
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs.
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 in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
Emerging knowledge of software applications and technical processes within a technical discipline (e.g. cloud, artificial intelligence, machine learning, mobile, etc.)

Nice To Haves

Experience with event-driven architectures and message processing patterns (idempotency, DLQ handling, backoff/retry strategies)
Infrastructure-as-Code exposure (e.g., Terraform/CloudFormation/CDK) and production operations experience (incident response, on-call, postmortems)
API design maturity (OpenAPI/Swagger, backward compatibility/versioning, contract testing)
Performance tuning experience across UI, JVM, and database layers
Strong grounding in OOP, design patterns, and microservices best practices

Responsibilities

Execute standard software solutions, design, development, and technical troubleshooting
Design, build, and maintain full-stack features across UI and backend services (React or Java 17 / Spring Boot).
Develop and operate microservices including API design, service decomposition, and reliability patterns (timeouts, retries, idempotency, DLQs).
Build integrations using AWS services such as API Gateway, SQS, SNS, S3, and RDS; troubleshoot production issues across services and infrastructure.
Produce clear design/architecture artifacts (service boundaries, data flows, sequence diagrams, ADRs) and ensure implementation aligns with design constraints.
Designs, develops, codes, and troubleshoots with consideration of upstream and downstream systems and technical implications
Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards
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.
Create secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
Proactively identify hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
Add to team culture of diversity, opportunity, inclusion, and respect