You will be taken to JPMorgan Chase’s own careers page to apply. Job ID 210797404.
Job description
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Software Engineer III - Automation Test Engineer + Mainframe 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 are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. Job responsibilities
Perform testing of core banking applications on the mainframe by designing and executing comprehensive test strategies.
Modeling real-world client usage, automating tests across multiple technologies, and validating system capacity and resilience.
Collaborate with stakeholders to review requirements, ensure production stability, and proactively identify opportunities for optimization and growth, supporting the reliability and scalability of critical banking services.
Drive technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse data sets.
Proactively identifies hidden problems and patterns in data and use these insights to drive improvements to application performance or system hygiene.
Present results at a technical and business level, comfortably call out risks, performance bottlenecks, or tuning recommendations.
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.
Required qualifications, capabilities, and skills:
Formal training or certification on software engineering concepts and 3+ years applied experience
Hands-on practical experience in mainframe manual testing, and operational stability.
Strong basic and conceptual knowledge of performance testing.
Experience in building & developing test approaches, and designing test plans and test scenarios.
Measurements of application performance and processing ceilings to identify tuning opportunities or prepare for growth.
Ability to understand client behavior, use cases, and build load models with appropriate transactions per second, or # of concurrent users, understand/apply pacing vs think time, triangulate geographical positions with latency, and chaos engineering.
Understand application dependencies/cross-impacts and multiple tech stacks.
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.
Preferred qualifications, capabilities, and skills
Additional skills of benefit would be experience in zSeries Mainframe, IBM MQ, or BMC MainView.
Experience in LoadRunner, DynaTrace, IBM MainView, MQ, and zSeries mainframe.
Must be able to gain a clear understanding of product architecture and application flows in complex legacy platforms and cloud technologies.
Overall knowledge of the Software Development Life Cycle and agile methodologies such as CI/CD, Application Resiliency, and Security. Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
