JPMorgan Chase—Software Engineer III - Java, UI, SQL, Kafka, Spark

Job description

Description: 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 - Java Full Stack Developer at JPMorgan Chase within the Commercial & Investment Bank, you'll be a part of an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role. Job responsibilities - Design, build, and maintain end-to-end full-stack solutions using Java technologies and modern UI frameworks. - Develop and support backend services and APIs (REST/GraphQL as applicable), including authentication/authorization, integration patterns, and error handling. - Leverages 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 - Build responsive, accessible frontend experiences and reusable UI components aligned to engineering standards. - Enable data-centric use cases by integrating with data platforms, data services, and event streams to expose reliable datasets and metrics to upstream/downstream consumers. - Collaborate with the data horizontal team to improve data quality, observability, lineage, and governance where required by the applications. - Apply strong engineering practices: code reviews, unit/integration testing, CI/CD, performance tuning, and production support. - Contribute to system design discussions and drive non-functional requirements (security, resiliency, scalability, latency). - Troubleshoot complex production issues across UI, services, and data interactions; implement sustainable fixes and automation. Required qualifications, capabilities, and skills - Formal training or certification on software engineering concepts and 2+ years applied experience - Professional software engineering experience with significant full-stack delivery. - Strong proficiency in Java and enterprise backend development (e.g., Spring / Spring Boot). - Experience building microservices and well-designed APIs (REST). - Solid understanding of frontend development with at least one modern framework (e.g., React, Angular, or Vue) plus HTML/CSS/TypeScript/JavaScript. - Strong knowledge of relational databases and SQL (e.g., PostgreSQL/Oracle), including schema design and query optimization. - Hands-on experience with engineering practices such as CI/CD pipelines, automated testing, and source control (Git). - Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate 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. - Practical knowledge of security concepts (OAuth2/JWT, secure coding, secrets management) and production readiness. - Strong problem-solving skills, ability to work across teams, and excellent written/verbal communication. Preferred qualifications, capabilities, and skills - Experience with data engineering and data platform integrations, such as: Messaging/streaming: Kafka (or equivalent), Data processing: Spark (or equivalent), Data warehousing/lakes: Snowflake/Databricks/Hive (or similar), Orchestration: Airflow (or similar) - Familiarity with data governance concepts: metadata/lineage, data quality checks, access controls, and auditability. - Experience with observability tooling: centralized logging, metrics, tracing (e.g., OpenTelemetry concepts). - Containerization and orchestration: Docker and Kubernetes (or equivalent). - Domain exposure to loan origination / servicing systems or regulated financial workflows (helpful but not required).