JPMorgan Chase
From JPMorgan Chase's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
We're looking for a hands-on Python Developers ready to take their career to new heights.
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
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
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