Lead Software Engineer - Data / Python / PySpark

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 JPMorganChase within Finance Technology, 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 Execute creative software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems Develop secure high-quality production code, and review and debug code written by others Identify opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems Lead evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture Lead communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies Add to team culture of diversity, opportunity, inclusion, and respect Drive 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 Apply 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 advanced applied experience Strong software engineering fundamentals — system design, data structures, algorithms, and architectural thinking — with the ability to ramp up quickly across varied tech stacks and project types Hands-on practical experience in full-stack or cross-functional development, spanning frontend, backend, data pipelines, or mobile, with demonstrated ability to contribute across the length and breadth of a project Proficiency in one or more of the following: Java/Spring Boot, Python/PySpark, GraphQL, or mobile development frameworks, with openness and aptitude to work across others as needed Experience with cloud platforms (preferably AWS) and working knowledge of both relational and distributed data platforms (e.g., Oracle, Databricks) Advanced understanding of agile methodologies, CI/CD pipelines, application resiliency, and secure software development practices Proficient in all aspects of the Software Development Life Cycle, including design, development, testing, and operational stability in production environments Practical understanding of AI/ML concepts and experience integrating or embedding AI/ML capabilities into business applications or data workflows, with the ability to identify opportunities where intelligent automation or predictive solutions can add business value 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 In-depth knowledge of the financial services industry and their IT systems Preferred qualifications, capabilities, and skills Experience with high-volume batch and real-time data processing using PySpark or similar frameworks, including performance tuning and troubleshooting of large-scale data pipelines In-depth knowledge of relational and distributed databases (e.g., Oracle, Databricks) with hands-on experience in query optimization, PL/SQL, and shell scripting in a cloud-native AWS environment Demonstrated ability to lead or contribute to architectural design discussions, technical evaluations, and proof-of-concept initiatives across diverse technology domains Experience working in or alongside financial services technology teams, with an understanding of regulatory, compliance, and security considerations in enterprise-grade systems