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.)