Staff Analytics Engineer

Join Pleo as a Staff Analytics Engineer in London, driving data services and analytics standards to revolutionize financial management.

• You can hold a standard without needing it applied perfectly, and you know which compromises are fatal and which are just untidy.
• You genuinely enjoy growing a community of practice through influence rather than authority and you take pride in seeing colleagues adopt and grow standards.
• You find the "how do we make data legible to an agent, not just to an analyst" problem interesting in its own right, rather than as a trend to keep up with.
• You want to own a domain. The Intelligence teams have strong AEs doing that. This role is for someone who wants to own the standards they build to.
• You advise others to use AI coding tools but don't use them yourself. AI-native development is part of the mandate, not a differentiating nice-to-have.
• You are more comfortable being consulted than being accountable. The semantic layer and modelling standards you set have downstream consequences across the whole function and in AI features customers use.
• English first. Since it's our company language, please submit your application in English. You’ll be using it a lot if you join us.
• A fair look for everyone. Our talent team reads every single application to ensure the process is fair. To keep things running smoothly, we only accept applications through our system—our support team can’t pass on calls or emails.
• Diversity drives us. We can only reach our goals if our team reflects the world around us. That starts with you hitting apply, even if you don't tick every single box. We encourage people from all backgrounds and experiences to join us.
• Interview at your best. We want you to feel comfortable throughout the process. If you have any accessibility requirements or need a specific format, email ----- We’ll design a process that works for you.
• Your data is safe. When you apply, we process your personal data as a data processor. For more information on how Pleo processes personal data, read our Privacy Policy here.
• Applying for multiple roles? Nothing is stopping you, and we assess every role independently. However, we do look for alignment, so make sure you can explain why your interest and experience are right for each specific role.
• Reapplying. If you’re applying for the same role again, please wait six months from your last decision before hitting submit.

• Deep demonstrated expertise in dbt and modelling practices: not just strong modelling, but a formed view of what modelling architecture should look like across a function. You can defend trade-offs, teach them, and enforce them in teams you don't manage.
• Deep BigQuery and SQL expertise, including performance, cost considerations, and the architectural challenges of complex analytical domains.
• Real experience owning a semantic layer (LookML, dbt MetricFlow, or equivalent). You have a point of view on what metric consistency should look like at scale, what breaks when it doesn't, and how to design it so AI tooling can consume it without degrading trust.
• AI-native development practice in a data engineering context. You use AI coding tools (Claude Code, GitHub Copilot, or equivalent) as a genuine part of how you work, and you can be specific about where they add real value and where they introduce risk in a governed analytics codebase.
• A track record of setting standards across teams you don't directly manage and making them stick by leveraging effective influencing techniques as opposed to relying on authority.
• Understanding of what LLMs and agentic tools need from a data layer such as how to model data, write documentation, and define metrics so that AI tools get consistent answers at runtime.
• Proven experience engaging credibly with backend engineers on data contracts and with senior stakeholders on what the semantic layer strategy means for the business.