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Senior Lead Software Engineer - Python

JPMorgan Chase
Glasgow, GBR
Full-time
Senior
Onsite
Discovered 1 weeks ago
awslinuxpythonsql

Full Job Posting

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
  • 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

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
  • Familiarity with large-scale Red Hat Enterprise Linux estate upgrades and rollout strategies

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

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