Sr. Lead Software Engineer- Python Backend, OOP - Front Office Trading

Job Description

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

• Architects and implements scalable, high-performance Python solutions for derivatives migration on the Athena platform, ensuring code quality, security, and operational stability.
• Develops and maintains object-oriented data models, persistence layers, and APIs to support cross-asset trade and risk workflows.
• Writes, reviews, and debugs production-quality Python code, driving engineering best practices including automated testing, CI/CD, and performance optimization.
• Serves as a function-wide technical subject matter expert in Python development and object-oriented database design within the Credit Technology domain.
• Collaborates directly with quantitative analysts and traders to translate pricing models and business requirements into production-grade engineered solutions.
• Provides technical guidance and mentorship to engineers through code reviews, design discussions, and advocacy of firmwide frameworks and Software Development Life Cycle practices.
• Adds to the team culture of diversity, equity, inclusion, and respect.
• Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Advanced proficiency in Python with deep understanding of object-oriented programming principles, design patterns, and best practices.
• Hands-on practical experience delivering system design, application development, testing, and operational stability in a front-office or trading technology environment.
• Strong experience with object-oriented databases or relational databases with ORM frameworks, including schema design and performance optimization.
• Experience with distributed/grid computing architectures for real-time risk calculations or large-scale data processing.
• Familiarity with software development lifecycle practices including CI/CD pipelines, automated testing, version control, and code review processes.
• Ability to tackle design and functionality problems independently with little to no oversight.
• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code 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 in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations experience coaching senior engineers/leads on compliant usage patterns and controls.

• If Python is not the primary language, then advanced proficiency in another strongly-typed or object-oriented programming language (e.g., Java, C++, C#, Kotlin) with willingness to transition to Python.
• Credit Derivatives subject matter expertise or equivalent expertise in another complex asset class.
• Experience with cross-asset risk, pricing, or trade management platforms (e.g., Athena or similar) and familiarity with derivative pricing models or risk metrics.
• Practical cloud-native experience (AWS, GCP, or Azure) and curiosity to deepen understanding of Credit business products and electronic/algorithmic trading workflows.