Lead Python Engineer (Sports Analytics)

Lead Python Engineer (one day a week in the central London office)

This role is a unique opportunity to combine technical challenges with creativity in a collaborative, high-standard work environment. By joining this team, you’ll not only be part of a creative and open work culture focused on innovation and excellence but also have the chance to work with and collaborate with some of the most well-known footballers in the industry.

This position offers significant opportunities for professional growth within sports analytics and the potential to impact sports performance through advanced technology, making it an ideal setting for those passionate about leveraging cutting-edge technology to make meaningful contributions in the world of sports analytics.

Ideal for those eager to blend their passion for sports with cutting-edge technology, aiming to make significant contributions in sports analytics. If you’re a detail-oriented innovator and team player, we’re excited to hear from you.

Responsibilities for the role of Lead Python Engineer will include:

• Lead the design of complex systems, ensuring scalability, performance, and maintainability using Python, along with frameworks like Django, Tornado or Flask.
• Integrate applications with Cloud services, utilising Docker for deployment and orchestration.
• Employ Agile methodologies to ensure the adoption of best practices in software development, including CI/CD pipelines using tools such as GitHub Actions or Jenkins.

As the selected Lead Python Engineer, your experience will include:

• Experience leading technical projects and mentoring Engineers.
• Software development industry experience, with a strong focus on Python.
• Experience with Cloud and Docker for deployment.
• Solid understanding of SDLC and CI/CD processes.
• Desired: experience in Infrastructure as Code developments and tools e.g. Terraform.
• Desired: experience with MLOps deployment and maintenance.
• Desired: Data Engineering technologies e.g. ETL, Spark, Dataflow, BigQuery.

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