Senior Machine Learning DataOps Engineer

The role 

Focussing on our Sportsbook product, our ML DataOps will play a critical role in streamlining and optimising data management processes, ensuring faster, more reliable, and data-driven decision-making. Given the dynamic nature of the industry, leveraging real-time insights, maintaining data integrity, and enhancing operational efficiency are essential for delivering a seamless customer experience while ensuring regulatory compliance

We are now at the next stage of our journey, building our capability for developing end-to-end machine learning data products, from research conducted by our Data Scientists through to production applications built by our Machine Learning Engineers. Our ML DataOps will align with cross-functional product teams and contribute to the development of high-quality Sportsbook data products. We are currently focusing on areas including personalisation and relevancy, and customer risk management.

Additionally, we are building the core toolset and capability required for our product and data engineering teams to scale efficiently and iterate rapidly. This includes developing a Data Lakehouse for use in both research and production contexts, and building the tooling we need to build, monitor and test our data products.

Your experience 

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

• 3-5 years relevant commercial experience.
• Excellent Java skills.
• Experience working alongside Data Engineers and ML Engineers.
• Experience with version control systems and CI/CD pipelines.
• Infrastructure as Code experience (Terraform or CloudFormation).
• Experience using Spark.
• Experience using Kafka (including Kafka Streams).
• Data Engineering experience.
• Understanding of the machine learning model lifecycle and operations (MLOps).
• Experience in cloud computing, ideally AWS.
• Passionate about your personal development.
• Excellent interpersonal skills: able to collaborate, explain complex topics and contribute to the success of cross-functional DE/MLE product teams.
• Ability to deal with and account for uncertainty, with the flexibility to learn by iteration. Proactive.

In addition, it would be an advantage if you also have:

• A knowledge of enterprise data concepts, including Data Mesh.
• AWS certification.
• An interest in mentoring and coaching more junior team members.