Data Engineer

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At Cleo, we’re not just building another fintech app. We're embarking on a mission to fundamentally change humanity’s relationship with money, giving everyone 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 2× year‑over‑year. Our team is made up of brilliant, driven individuals who are passionate about making a real difference.

In this role, you’ll work closely with product teams to ensure they effectively adopt the tools, frameworks, and processes provided by the Data Platform team. You’ll enable them to build scalable, efficient, and reliable data and ML solutions, achieve their OKRs, and champion best practices in Data Engineering.

At the same time, you’ll act as a crucial bridge between product teams and the Data Platform team, gathering insights on real‑world challenges, gaps, and pain points in the existing platform, and collaborating on continuous improvement of our internal tooling and infrastructure.

• Facilitate the adoption of the Data Platform team’s tools, frameworks, and processes to achieve product teams’ OKRs.
• Guide teams in implementing robust data pipelines, reliable model deployment workflows, effective monitoring strategies, and cost‑efficient practices.
• Break down complex problems and propose well‑reasoned data system designs, considering scalability, maintainability, and appropriate tooling.
• Act as a critical feedback loop, gathering insights on platform challenges and communicating them to the Data Platform team for continuous improvement.
• Champion software engineering best practices, including code quality, maintainability, version control, and automation, with proficiency in Python.
• Collaborate with Data Scientists, Software Engineers, and Product Managers to align data initiatives with business goals.
• Share knowledge and help develop engineering capabilities across the broader organization.

• Proven ability to design and break down complex data systems, focusing on simplicity, maintainability, and scalability.
• Solid proficiency in Python and adherence to software engineering best practices.
• Knowledge of at least one distributed processing framework (e.g., PySpark), with a bonus for streaming frameworks (e.g., Flink).
• Practical experience with Docker and Kubernetes.
• Experience with IaC tools, specifically Terraform.
• Good understanding of different storage types and when to use them (e.g., OLTP, OLAP, S3).
• Understanding of core computer science concepts including Data Structures, Algorithms, Time Complexity, and Distributed Processing.
• Demonstrated ability to work effectively across teams (data scientists, software engineers, PMs).
• Understanding the value proposition of data solutions and adopting a customer‑focused, incremental delivery approach.

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Location:

London, England, United Kingdom.

Seniority level:
Mid‑Senior level |

Employment type:

Full‑time | Job function:
Information Technology