ML/DataOps Manager

Lead and grow a team of DataOps and MLOps engineers, providing technical direction, coaching, and career development.

Define and drive the strategy for DataOps and MLOps capabilities across the sportsbook data platform.

Build and evolve the platform and tooling that supports end-to-end machine learning lifecycle management (development, deployment, monitoring, and retraining).

Establish best practices for data and machine learning workflows, including automated testing, deployment, and rollback strategies.

Work with DevOps to evolve infrastructure as code practices (e.g. Terraform, CloudFormation) to ensure scalable and reproducible environments.

Collaborate with Data Engineers, Quants, and ML Engineers to improve developer experience, platform usability, and delivery velocity.

Implement robust monitoring, observability, and alerting across data pipelines and ML models (including model performance and drift).

Drive standardisation of tooling and workflows across teams, balancing flexibility with consistency.

Ensure data and model governance practices are embedded, including reproducibility, lineage, discoverability, and compliance.

Partner with product and business stakeholders to align platform capabilities with strategic priorities.

Manage platform reliability, performance, and cost efficiency within AWS.

A modern data and machine learning platform supporting both batch and real-time use cases.

Tooling and infrastructure for experiment tracking, model deployment, feature management, and monitoring.

A Data Lakehouse architecture supporting both analytical and ML workloads.

CI/CD pipelines and infrastructure that enable rapid, safe iteration for data and ML teams.

Cross-team enablement to improve how data and ML products are developed, deployed, and operated.

We think that to be successful in this role you will be able to demonstrate many of the following attributes:

5+ years of relevant commercial experience, including experience in DataOps, MLOps, or platform engineering roles.

Proven experience leading or mentoring engineers in a technical environment.

Strong understanding of data engineering and machine learning workflows and how they operate in production.

Experience implementing CI/CD pipelines for data and/or machine learning systems.

Experience with Infrastructure as Code (Terraform, CloudFormation, or similar).

Strong experience with cloud platforms, ideally AWS.

Experience with distributed data processing technologies (e.g. Spark) and streaming platforms (e.g. Kafka).

Ability to operate at both a strategic and hands-on level when required.

Strong communication and stakeholder management skills, with the ability to align cross-functional teams.

Proactive mindset with the ability to navigate ambiguity and drive continuous improvement.

Experience in sports betting, trading platforms, or regulated industries.

Familiarity with modern ML tooling (e.g. feature stores, model registries, experiment tracking platforms).

Knowledge of data architecture patterns such as Data Mesh or medallion architecture.

Experience with observability and monitoring tools for data and ML systems.

Experience building internal platforms or developer tooling.

Passion for mentoring and building high-performing teams.

This role is critical to scaling how we build and operate data and machine learning products across sportsbook. By establishing strong DataOps and MLOps foundations, you’ll enable teams to deliver high-quality, reliable, and production-ready solutions faster. This directly impacts our ability to personalise customer experiences, manage risk effectively, and make better data-driven decisions across the business.

We believe talent knows no boundaries. Our hiring process focuses solely on your skills, experience, and potential to contribute to our team. We welcome applicants from all backgrounds and evaluate each candidate based on merit, regardless of personal characteristics as the age, gender, origin, religion, sexual orientation, neurodiversity or disability.