Lead Software Engineer - Python​/Athena Trade and Risk Services

JOB DESCRIPTION

Join the ATRS Tools team and help shape the developer and operations experience on one of the firm's most strategic technology platforms, used by thousands of engineers and middle-office users every day. Our remit spans two pillars: engineering productivity tooling that gives developers confidence in the changes they make, and operational tooling that gives the business real-time visibility and control over the platform.

The tools we build touch daily activity across the firm's front-to-back platform. If you want your work to be visible across the organization and directly influence how a codebase of this scale is operated, tested, and evolved, this is the place.

As a Lead Software Engineer on ATRS Tools, you will design and build systems that give engineers confidence in the changes they make and give Operations users the real-time tooling they need to keep the business running. You will partner across platform engineering, LOB teams, Controls, and Audit to deliver low-friction, high-impact platform capabilities. Our culture values technical ownership, product thinking, and pragmatic delivery;

we ship vertical slices, prove them end-to-end, then broaden.

• Build and evolve engineering productivity tooling: systems that help developers make changes with confidence, covering change management, regression and behaviour-comparison testing, and codebase health at scale
• Build and evolve operational tooling: real-time monitoring, alerting, and workflow frameworks that give Operations users the visibility and control they need to run the business
• Drive strategic platform initiatives that reshape how core capabilities, such as runtime configuration and large-scale codebase cleanup, are managed safely and deterministically across the estate
• Design and evolve real-time, event-driven systems built on modern streaming platforms that power operational monitoring and downstream consumers across lines of business
• Embed AI capabilities into team-owned products, surfacing intelligent recommendations, accelerating routine user actions, and turning ambiguous inputs into structured, reviewable outputs, using approved enterprise AI tooling
• Own features across their full lifecycle: discovery, design, implementation, rollout, telemetry, and operational support (including production incident response for team-owned components)
• Translate ambiguous asks from Operations teams, LOB engineers, and Controls partners into reusable platform capabilities, generalising per-team patterns into framework primitives adopted across the estate
• Partner with Controls and Audit stakeholders to implement effective, low-friction engineering controls in change management and release workflows
• Influence engineering standards and best practices across a broad developer community through RFCs, guidance, and reference implementations
• Drives 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.
• Applies 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.

• Formal training or certification on software engineering concepts and advanced applied experience
• Strong software engineering fundamentals and a genuine passion for developer and operations tooling
• Proficiency in Python, with working knowledge of Type Script/React for web tooling and SQL for full-stack work
• Proficient user of enterprise-authorized AI-assisted development tools (coding, review, test acceleration, troubleshooting), with the ability to set team expectations for validating AI outputs for correctness, performance, and security
• Strong understanding of responsible AI use when building AI capabilities into products, including data sensitivity, secure handling of inputs and outputs, and resiliency and security expectations, along with the ability to guide a team through these considerations
• Solid grounding in testing, reliability, and maintainable system design, comfortable with layered architectures, event-driven systems, and distributed state
• Ability to operate independently in ambiguous problem spaces (RFCs, greenfield platform initiatives) and collaborate effectively across teams
• Skill in turning loosely defined requirements into robust, widely adopted solutions: vertical slice first, broaden second
• Working knowledge of modern engineering workflows: testing, CI/CD, static analysis, version control, and deployment
• Demonstrated experience leading effective use of approved AI-assisted software development tools…