Full Stack Lead Engineer - App, Data & AI Engineering

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