Data Engineer/MLOps Engineer UK or Poland

At Cleo, we are embarking on a mission to fundamentally change humanity's relationship with money. Imagine a world where everyone, regardless of background or income, has access to a hyper-intelligent financial advisor in their pocket. Cleo is a profitable, fast-growing unicorn with over $200 million in ARR and growing over 2x year-over-year. This is a chance to join a team of brilliant, driven individuals who are passionate about making a real difference.

We are hiring a Data Engineer / MLOps Engineer to support Cleo's product teams in delivering impactful data-driven solutions. In this role, you will help teams adopt our internal Data Platform, build efficient data pipelines, and deploy machine learning models effectively at scale. You will serve as a bridge between product teams and our Data Platform team, ensuring tools and infrastructure meet real-world needs and continuously evolve. You will combine hands-on technical work with strategic collaboration, directly influencing how Cleo leverages data to achieve product and business goals.

What you will do:

• 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.

What you will need:

• 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.

Nice-to-Haves:

• 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.

What We Offer:

• Competitive compensation (base + equity), with clear progression frameworks and bi-annual reviews.
• Flexible working arrangements—hybrid if you are 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.

We strongly encourage applications from people of colour, the LGBTQ+ community, people with disabilities, neurodivergent people, parents, carers, and people from lower socio-economic backgrounds. If there is anything we can do to accommodate your specific situation, please let us know.