Software Engineer III - Java / Data

Design and deliver market-leading technology products in a secure and scalable way as a seasoned member of an agile team

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

Role Summary

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.