Role Overview

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.

Key Responsibilities

• Design, implement, and maintain production-grade Python systems supporting power price forecasting pipelines, optimisation and dispatch of flexible assets (BESS, gas, hydro), and trading, simulation, and backtesting environments.
• Establish and uphold strong Python engineering standards across the analytics codebase, including clear package and module structure, well-defined interfaces between models, data, and execution layers, and consistent error handling, logging, and observability.
• Lead improvements in test coverage and reliability, spanning unit, integration, and regression testing for quantitative models.
• Proactively identify, prioritise, and shape platform enhancements that effectively support the trading roadmap, balancing urgency, technical debt, and cross-team impact.
• Work closely with quants to refactor research-driven Python code into maintainable, extensible components without compromising model fidelity.
• Optimise performance-critical Python code paths, including efficient use of vectorisation, parallelism, and memory management.
• Collaborate with DevOps and Data Engineering on deployment, CI/CD, and runtime monitoring of Python services and batch workflows.
• Provide technical leadership and mentorship on Python best practices, raising the overall engineering maturity of the analytics team.

Required Skills and Experience

• 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, environment isolation, testing frameworks, profiling, and performance optimisation.
• Solid grounding in software engineering fundamentals: system and API design, code readability, maintainability, extensibility, version control, and collaborative workflows.
• Experience working in quantitative, scientific, or data-intensive domains.
• Ability to operate comfortably at the interface between research, engineering, and commercial use cases.

Desirable Experience

• 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.

Personal Attributes

• Strong engineering judgement with a pragmatic approach to complexity.
• Comfortable taking ownership of critical systems used in live decision-making.
• Able to challenge constructively and influence technical direction without over-engineering.
• Motivated by applying high-quality software engineering to problems with tangible commercial and system-wide impact.