Software Engineer III - Java / Data

As a Software Engineer III at JPMorgan Chase within J.P. Morgan Chase's Corporate Technology — Data Core Engineering (DCE) group, 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 are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. You are a senior hands-on technical contributor who designs, builds, and operates the platforms that power data controls, cataloging, and core data services across the firm. You own solutions end to end: you write and review production code, make architectural decisions, and stand behind the reliability of what you ship. This is a builder's role — engineering is the primary mandate, and operational excellence is the natural outcome of the way you engineer.

Job Responsibilities

• Designs, develops, and delivers secure, high-quality production code for distributed microservices, and review and debug code written by others.
• Makes and defends architectural and design decisions, balancing delivery speed with long-term maintainability, resiliency, and cost.
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• 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.
• Builds reliability into the software from the start — instrument services with meaningful telemetry, define and track service-level objectives, and design for failure with patterns such as graceful degradation, circuit breakers, retries, and redundancy.
• Eliminates operational toil by engineering automation, tooling, and self-healing workflows rather than absorbing manual work.
• Owns the run-side outcomes of your services: lead incident triage and root-cause analysis, drive permanent fixes for recurring defects, and feed learnings from post-incident reviews back into the codebase.
• Develops and maintain infrastructure-as-code to automate provisioning and configuration across AWS environments.
• Partner with product and platform teams to translate business needs into technical designs, and represent engineering trade-offs in cross-functional forums.
• Mentors and uplift engineers, promote reuse of patterns, and raise the bar on engineering and reliability practices across the team.
• Participates in a shared on-call rotation for the services the team owns, using each engagement as input to improve system design.

Required Qualifications, Capabilities, and Skills

• Formal training or certification on software engineering concepts and 3+ years applied experience 
• Advanced, hands-on proficiency in Java and the Spring ecosystem, building RESTful microservices and object-oriented, pattern-based designs.
• Strong, hands-on experience engineering and operating workloads on AWS (e.g., EKS, ECS, Lambda, API Gateway, RDS, CloudWatch), including deploying and running production services in the cloud.
• 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 for correctness, performance, and security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
• Strong experience with containerization and orchestration (Docker, Kubernetes).
• Practical experience with a NoSQL and/or relational database at production scale.
• Depth in the full software development lifecycle — CI/CD, automated testing, code review, refactoring, and secure coding practices.
• Working knowledge of observability tooling (e.g., Dynatrace, Splunk, or OpenTelemetry/Grafana/Prometheus) and the ability to design monitoring and alerting into services.
• Fluency with CI/CD tooling (e.g., Jenkins), build tools (Maven/Gradle), and source control (Git).
• Strong problem-solving and analytical skills, a delivery-focused approach, and the ability to operate with minimal direction.

Preferred Qualifications, Capabilities, and Skills

• Experience with event-streaming technologies (e.g., Kafka) and event-driven architectures.
• Familiarity with reliability engineering practices — SLIs/SLOs, error budgets, disaster recovery and high-availability design, and failure-mode analysis.
• Exposure to AI-assisted development or operational workflows and to modern developer tooling.
• Knowledge of the financial services industry and its regulatory and control requirements.