Senior Lead Software Engineer - Python

We're looking for a hands-on Python Developers ready to take their career to new heights. Join the ranks of top talent at one of the world's most influential companies. As a Senior Lead Software Engineer at JPMorgan Chase within the Athena Rates platform team, You’ll build Python tooling, improve platform capabilities, and partner closely with developers, quantitative teams, and production stakeholders. The work is varied and high-impact: cloud migrations, operating system and library upgrades, security-driven remediation, capacity planning, hygiene, database management and rapid production problem-solving. You’ll often deliver changes end to end, and sometimes coordinate with service owners to get outcomes landed. We use AI tooling daily to accelerate analysis and implementation, and progressing toward more agentic ways of working. Job responsibilities Build and maintain Python tooling, services, and automation that support Athena Rates platform delivery and operations Provide platform guidance to developers and quantitative teams, including how to use infrastructure safely and effectively Design and evolve APIs and interfaces that support application integrations Deliver capacity planning and performance analysis, translating findings into concrete engineering improvements Execute modernization work across Linux and cloud infrastructure, including migrations and replacement of end-of-life components Plan and deliver operating system, middleware, and library upgrades with strong change discipline and stakeholder alignment Remediate certificate, credential, and access issues through secure, repeatable processes and automation Improve platform hygiene and controls (for example, environment segregation and prevention of unsafe cross-environment usage) Support production incidents at third-line level, providing rapid mitigation and leading long-term remediation Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards Coordinate across multiple teams and component owners to land outcomes when dependencies sit outside your direct control Contribute to cost awareness by helping manage infrastructure usage, budget considerations, and efficiency opportunities Required qualifications, capabilities and skills Advanced Python software engineering experience delivering production-grade tooling and services Strong Linux systems knowledge, including troubleshooting performance, processes, services, and networking fundamentals Experience delivering controlled change in production environments (migrations, upgrades, platform hygiene, automation) Experience diagnosing complex production issues end to end, including stabilization and root-cause remediation Experience partnering with developers and production stakeholders to deliver enablement tooling and clear operating guidance Experience designing or supporting APIs and integrations used by downstream engineering or quantitative teams Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, Working knowledge of SQL and familiarity with integrating across multiple data technologies (SQL and in-memory patterns) Strong ownership and problem-solving skills, with the ability to take ambiguous goals and drive them to completion Clear written and verbal communication skills across technical and non-technical stakeholders Preferred qualifications, capabilities and skills Experience modernizing legacy integration mechanisms (for example, file transfer stacks and messaging components) Familiarity with large-scale Red Hat Enterprise Linux estate upgrades and rollout strategies Experience building database hygiene tooling and operational guardrails integrated into application workflows Experience implementing secure credential management patterns (for example, vault migrations, key rotation automation) Experience upgrading batch scheduling or compute grid platforms and related tooling Practical experience with cloud infrastructure (for example, AWS) and operating hybrid estates Demonstrated use of AI tooling in daily engineering work to improve speed and quality Experience leading multi-workstream modernization portfolios driven by cyber resilience priorities