Data Engineer, MLOps Engineer

Summary

Join Cleo, a fast-growing fintech unicorn, as a Data Engineer / MLOps Engineer. You will collaborate with product teams to build efficient data pipelines and deploy machine learning models. This role requires strong data system design knowledge, proficiency in Python, and experience with containerization and orchestration. You will guide teams in adopting best practices and contribute to enhancing data and ML infrastructure. The ideal candidate possesses a product mindset and excellent communication skills. Cleo offers competitive compensation, flexible working arrangements, generous leave, and various benefits.

Requirements

• Strong knowledge of data system design; ability to break down problems and propose effective solutions
• Proficiency in Python, with a strong understanding of software engineering best practices (testing, automation, code quality)
• Experience with containerisation and orchestration (Docker and Kubernetes)
• Infrastructure as Code (Terraform or similar)
• Experience with at least one distributed data-processing framework (Spark, Flink, Kafka, etc.)
• Familiarity with different storage solutions (e.g., OLTP, OLAP, NoSQL, object storage) and their trade-offs
• Product mindset and ability to link technical decisions to business impact
• Excellent cross-functional communication—able to partner with data scientists, software engineers, and product managers

Responsibilities

• Collaborate closely with product teams to implement robust, scalable data pipelines and ML workflows
• Guide teams in adopting best practices around data engineering, infrastructure management, and MLOps
• Surface practical insights from product teams to inform improvements in our internal Data Platform
• Contribute actively to enhancing our data and ML infrastructure—focusing on usability, efficiency, reliability, and cost-effectiveness
• Mentor and support engineers and data scientists in data engineering and MLOps best practices

Preferred Qualifications

• Experience with streaming platforms and understanding stream/table transformations
• Familiarity with ML system deployment and management (Kubeflow, MLflow, Airflow, Flyte, etc.)
• Knowledge of monitoring, alerting, and operational best practices for data-intensive systems
• Experience with Feature Stores or similar ML data management tools

Benefits

• Competitive compensation (base + equity), with clear progression frameworks and bi-annual reviews
• Flexible working arrangements—hybrid if you're near London, fully remote elsewhere
• Generous annual leave (starting at 25 days + public holidays, increasing with tenure)
• Private medical insurance, enhanced parental leave, mental health support, employer-matched pension, and more
• A genuinely supportive, inclusive culture that encourages professional and personal growth