Data Engineering Manager - Quant Team

Job Description:

The Quant Team is focused on developing quantitative sport models, taking models from prototype to production. The team is an important component of the vision for the future evolution of the FJD UNITED Sportsbook Platform (KSP).

The Quant Team is split into three workstreams: Quant Data Engineering, Quant Research, Quant Model Development. We are looking to recruit a talented Data Engineering Manager to join the Data Engineering team. If you are passionate about leading high-performing teams to build cutting-edge data products, then this is the role for you!

Responsibilities :

• Oversee the delivery of scalable data pipelines related to handling sporting events and odds data, in order to create high-quality Quant data products that support both the Quant Research team and the wider Sportsbook.

• Plan out data engineering roadmaps and tasks on sprint-by-sprint, quarter-by-quarter and year-by-year timescales.

• Support Senior Managers, Product Owners and Technical Leads to ensure clear goals are consistently delivered upon.

• Line manage and coach Quant data engineers and Quant model engineers to support their professional development and encouraging their up-skilling.

• Provide technical advice and expertise to data engineers at all levels of seniority.

• Take an active role in recruitment, liaising with TA, preparing for interviews, interviewing candidates, etc.

• Split your time between planning and overseeing deliveries, people management and personal contributions.

• Dedicate time for investigating new techniques and tooling that can benefit the wider team.

• Actively promote work from the Quant Data Engineering team across the broader business.

Desirable Attributes:

• A track record of delivering algorithmic data-driven products

• Previous experience managing a team of high-performing engineers.

• Experience developing solutions working with sporting data and/or demonstrable knowledge and interest in this area.

• Programming skills in Python and SQL (including OO and functional programming).

• Knowledge and experience in cloud computing, ideally AWS.

• Experience working with a range of database technologies, including PostgreSQL.

• Experience using Apache Spark for processing large datasets.

• Experience with frameworks and technologies used in component orchestration, such as Airflow.

• Experience applying software development best practices such as version control, unit testing, linting and CI/CD.

• Experience working with datalake architectures and associated tooling

• A knowledge of enterprise data concepts, including Data Mesh.

• Practical MLOps experience.

• Experience with data management tooling for cataloguing, lineage, governance, integrity and discoverability

• An interest in sports betting.

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