Software Engineer III - Java / Data

JPMorganChase•Houston, TX

About The Position

As a Software Engineer III in JPMorgan Chase’s Corporate Technology — Data Core Engineering group, you are a seasoned member of an agile team designing and delivering secure, stable, and scalable technology products. You are a senior hands-on technical contributor who designs, builds, and operates platforms supporting data controls, cataloging, and core data services across the firm. You own solutions end to end, including production code, architectural decisions, and the reliability of delivered services. Engineering is the primary focus, with operational excellence built into how you work.

Requirements

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

Nice To Haves

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

Responsibilities

Design, develop, deliver, review, and debug secure, high-quality production code for distributed microservices.
Make and defend architectural and design decisions that balance delivery speed with maintainability, resiliency, and cost.
Use enterprise-authorized AI coding tools to improve code quality, delivery speed, and productivity; validate outputs through peer review, automated testing, and secure coding standards, and share reusable learnings and patterns.
Apply software development lifecycle tools, including authorized AI-assisted development and automation capabilities, to improve the value of automation.
Build reliability into software through meaningful telemetry, service-level objectives, graceful degradation, circuit breakers, retries, and redundancy.
Reduce operational toil by engineering automation, tooling, and self-healing workflows.
Lead incident triage and root-cause analysis, drive permanent fixes for recurring defects, and incorporate post-incident learnings into the codebase.
Develop 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.
Mentor and support engineers, promote reusable patterns, and improve engineering and reliability practices across the team.
Participate in a shared on-call rotation for team-owned services and use operational engagements to improve system design.