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We are seeking a Senior Python Software Engineer/Quantitative Developer to take a leading role in the design, development, and evolution of the client's core analytics and trading platforms. The role is explicitly Python-centric, with a strong focus on software architecture, code quality, and reliability across complex quantitative systems. You will work embedded within the analytics and trading function, partnering closely with quantitative analysts and data scientists to turn research models into robust, production-grade Python systems. The role is intended to complement a mathematically strong team by bringing deeper software engineering rigour, helping scale both the codebase and the organisation as trading activity grows.
Job Responsibility:
Design, implement, and maintain production-grade Python systems supporting power price forecasting pipelines, optimisation and dispatch of flexible assets (BESS, gas, hydro), trading, simulation, and backtesting environments
Establish and uphold strong Python engineering standards across the analytics codebase
Lead improvements in test coverage and reliability
Proactively identify, prioritise, and shape platform enhancements
Work closely with quants to refactor research-driven Python code
Optimise performance-critical Python code paths
Collaborate with DevOps and Data Engineering on deployment, CI/CD, and runtime monitoring
Provide technical leadership and mentorship on Python best practices
Requirements:
Strong professional experience as a Python software engineer, working on large or long-lived codebases
Deep understanding of Python for production use, including packaging, dependency management, and environment isolation
Testing frameworks and test design for numerical code
Profiling and performance optimisation in Python
Solid grounding in software engineering fundamentals: system and API design, code readability, maintainability, and extensibility, version control and collaborative development workflows
Experience working in quantitative, scientific, or data-intensive domains
Ability to operate comfortably at the interface between research, engineering, and commercial use cases
Nice to have:
Experience in energy markets, commodity trading, or financial systems
Familiarity with optimisation libraries, numerical methods, or simulation frameworks in Python
Exposure to containerisation, cloud-native Python deployments, or distributed compute patterns
Prior experience modernising or hardening research-oriented Python codebases