Junior ML DataOps Engineer
At FDJ United, the ambition is to be the most insight-driven gambling company, and significant investments have been made in data capabilities. DataOps plays a critical role in streamlining and optimising data management processes for the Sportsbook product, ensuring faster, more reliable, and data-driven decision-making. In a dynamic and highly regulated industry, leveraging real-time insights, maintaining data integrity, and enhancing operational efficiency are essential for delivering a seamless customer experience.
As a Junior ML DataOps Engineer, the role involves helping to build, operate, and improve the tools, infrastructure, and workflows that enable Data Scientists, Data Engineers, and Machine Learning Engineers to deliver high-quality data and ML products at scale. This includes contributing to automating processes, improving data quality, developing testing and monitoring capabilities, and making development and deployment workflows more reliable and efficient.
The role also entails exploring and adopting new technologies, including Generative AI, to enhance how data products are built, tested, and operated. This includes contributing to Data Lakehouse capabilities and developing tooling to help teams work more efficiently, experiment safely, and move solutions into production with confidence. Working alongside experienced engineers, there is an opportunity to develop skills across software engineering, data engineering, cloud infrastructure, and AI operations.
- Build, operate, and improve the tools, infrastructure, and workflows that enable Data Scientists, Data Engineers, and Machine Learning Engineers to deliver high-quality data and ML products at scale.
- Contribute to automating processes, improving data quality, developing testing and monitoring capabilities, and making development and deployment workflows more reliable and efficient.
- Explore and adopt new technologies, including Generative AI, to improve how data products are built, tested, and operated.
- Contribute to Data Lakehouse capabilities and develop tooling that helps teams work more efficiently, experiment safely, and move solutions into production with confidence.
- Good programming skills in Python or Java (required)
- Understanding of software engineering fundamentals, including version control, code reviews, testing, and maintainable code (required)
- Interest in DataOps, DevOps, MLOps, or ML Engineering, and how these disciplines help teams deliver reliable production systems (required)
- Familiarity with CI/CD pipelines and automated software deployment, or a willingness to learn (required)
- Understanding of data pipelines, data processing, and the importance of data quality and reliability (required)
- Interest in AI and machine learning, including Generative AI and the operational challenges of taking AI solutions into production (required)
- Familiarity with cloud computing, ideally AWS, or experience with cloud-based projects (required)
- Proactive approach to problem-solving, with the ability to investigate issues, learn from feedback, and develop practical solutions (required)
- Good communication and interpersonal skills, with the ability to collaborate with engineers and other stakeholders across different disciplines (required)
- Growth mindset, with enthusiasm for learning new technologies and developing technical capabilities (required)
- Ability to work with uncertainty, adapt to changing priorities, and approach unfamiliar problems with curiosity and initiative (required)
- Experience with Python-based data processing or automation (nice-to-have)
- Familiarity with AI/ML frameworks, APIs, or Generative AI tools (nice-to-have)
- Exposure to Spark, Kafka, or other distributed data processing technologies (nice-to-have)
- Experience with Docker, containers, or Kubernetes (nice-to-have)
- Familiarity with infrastructure as code, such as Terraform or CloudFormation (nice-to-have)
- Exposure to AWS services and cloud-native application development (nice-to-have)
- Understanding of automated testing, data quality checks, monitoring, logging, or alerting (nice-to-have)
- Experience with Git-based workflows and tools such as GitLab (nice-to-have)
- Interest in data platforms, Data Lakehouse architectures, or enterprise data concepts (nice-to-have)
- Personal projects, academic work, or contributions that demonstrate curiosity and practical experience in software engineering, data, or AI (nice-to-have)
- Learning and development opportunities
- Social events and activities
- Inclusion networks
- Employee Assistance Programme
- Private medical insurance
- Long service awards
- Pension
FDJ United is one of Europe's largest gaming and betting groups, formed after French lottery operator La Française des Jeux (FDJ) acquired Kindred Group - the company behind Unibet - in 2024 and rebranded the enlarged group in 2025. Headquartered in Boulogne-Billancourt, France, it operates lottery, online sports betting, casino and poker across multiple European markets and Australia. Its portfolio includes well-known online brands such as Unibet, 32Red and Maria Casino, alongside FDJ's French lottery and retail network. The group is listed on the Euronext Paris exchange.