Senior Data Engineer - Commodities

We are seeking a highly skilled and motivated Front Office Python Developer to join our Quantitative Research and Trading team. This role is central to building and maintaining cutting-edge applications that enable quant researchers and traders to deploy models and strategies efficiently. You will work directly with front office stakeholders to design, develop, and optimize software solutions that support real-time analytics, model integration, and trading workflows. Key Responsibilities

• Collaborate closely with quantitative researchers to transform research prototypes into robust, production-ready applications.
• Design and develop Python-based tools and frameworks for model deployment, backtesting, and simulation environments.
• Build intuitive and performant front-end or visualization components for analytics and monitoring.
• Develop and maintain APIs and services to integrate models with trading systems and data platforms.
• Optimize application performance for low-latency and high-throughput environments.
• Ensure best practices in software engineering, including testing, documentation, and version control.
• Contribute to the continuous improvement of development processes, including CI/CD and automated deployment pipelines.

• Strong proficiency in Python, with experience in libraries such as Pandas, NumPy, and application frameworks (e.g., Flask, FastAPI, or similar).

• Solid understanding of software engineering principles, including object-oriented design and modular architecture.

• Experience building applications for front office environments within financial services.

• Familiarity with market data feeds (e.g., Bloomberg, Reuters, FIX) and tick-level data processing.

• Knowledge of SQL and experience with time-series databases (e.g., kdb+, TimescaleDB, or similar).

• Exposure to distributed systems, messaging frameworks (e.g., Kafka, ZeroMQ), and event-driven architectures.

• Excellent communication skills and ability to work effectively across quant, trading, and technology teams. Preferred Qualifications

• Degree in Computer Science, Engineering, Mathematics, or a related field.

• Familiarity with DevOps practices, containerization (Docker, Kubernetes), and cloud platforms.

• Understanding of quantitative finance concepts is a plus, but not required.