Lead Software Engineer- Automation testing

We have an opportunity to impact your career and provide an adventure where you can push the limits of what’s possible. 

As a Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. 

Job responsibilities

• Leads end-to-end product delivery processes including intake, dependency management, release management, product operationalization, delivery feasibility decision-making, and product performance reporting, while escalating opportunities to improve efficiencies and functional coordination
• Leads the completion of change management activities across functional partners and ensures adherence to the firm’s risk, controls, compliance, and regulatory requirements
• Manages timelines and dependencies while monitoring blockers, ensuring adequate resourcing, and liaising with stakeholders and functional partners. Assesses and communicate risks, propose mitigation strategies, and make recommendations for potential impacts.
• Mentors and guide team members, fostering a culture of continuous learning and improvement.
• Drives automation and E2E testing for key initiatives across web and mobile channels, ensuring high-quality delivery. Defines and present test strategies, building impactful decks and artifacts for leadership and stakeholders.
• Develops and implement automation solutions, best practices, and guidelines to ensure consistency and maintainability. Monitors and analyze automated test results, troubleshoot failures, and resolve issues to maintain system stability and performance.
• Coordinates and prioritize testing efforts, including test planning, execution, and defect management, to ensure timely product delivery.
• Manages projects by tracking progress, anticipating and eliminating roadblocks, and reporting results to stakeholders.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• 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 5+ years applied experience
• Demonstrated ability to execute operational management and change readiness activities. Strong understanding of delivery and a proven track record of implementing continuous improvement processes
• Experience in product or platform-wide release management, in addition to deployment processes and strategies
• Expertise in automation tools (Cucumber, Gherkin, Java, Selenium, Python, Playwright). Experience in Quality Assurance or User Acceptance Testing, with experience in BDD/TDD and test case automation. Functional knowledge of the Payments domain and hands-on experience in requirements, user stories, and test script execution.
• Proven experience with test automation tools for efficient E2E test execution. Financial Services testing experience across multiple lines of business in high-volume, complex environments. Advanced skills in defect management tools, JIRA (including dashboards and JQL), and Agile/SW development life cycle.
• Strong data analysis skills, experience with data visualization platforms, and a record of leading through change and ambiguity.
• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support solutions analysis workflows with strong validation habits and awareness of data sensitivity.
• Ability to review and validate AI-assisted analyses and recommendations before use, escalating when uncertain and following data handling expectations.
• Demonstrated experience leading effective use of approved AI-…