About PleoMessy spend management is tricky business. And tedious processes are a lose-lose situation for all involved, not just finance. We build spend solutions that make managing money seamless, empowering, and surprisingly effective for finance teams and employees alike - with a vision to help all businesses 'go beyond'.With great ambitions driving us forward, we can't say we've got this whole thing figured out. Please do not rush and use this time to submit a high quality application!About the roleThis is a senior individual contributor role in our Data Services & Governance team where you'll act as the thought and technical leader owning the semantic layer and analytics standards for Pleo. This means that you won't own a domain but you'll own what good looks like across all of them by developing, improving , maintaining and evangelising our modelling standards and AI-augmented development practices that every Analytics Engineer work with, regardless of which team they sit in. The semantic layer you will be designing and maintaining will be the single source of truth that AI agents, BI tools, and analysts query. This is foundational work with company-wide reach which will be ideal for you if you enjoy building things from the ground up. Our BI stack is also in transition so, you would not be inheriting a mature setup and maintaining it. You'd be deciding what it should be, then building it.For additional context, our tech stack currently include: GCP, BigQuery, dbt Core, Airflow, SQL, Python, Claude Code, GitHub Copilot.Who you'll work withYou'll report to the Data Engineering Manager who oversees the Data Infra & MLOps team as well as the Data Services & Governance team. Your primary relationship will be with Analytics Engineers embedded across the Intelligence function who should come to you for architecture guidance, semantic layer decisions, and standards questions. You will also partner with a Staff Data Engineer on data engineering standards and pipeline practices, and with others on self-serve analytics and BI tooling governance. You'll engage with the Data Serving team on entity definitions and with the GenAI Platform team on what AI-ready data looks like at the platform boundary.The goal is one canonical definition of Monthly recurring revenue (MRR), churn, transaction, customer - used by analysts, BI tools, and AI tools without divergence. Set data modelling standards for the function: what clean, layered, well-tested dbt architecture looks like across all domains, at all levels of complexity. Build and run the Analytics Engineering community of practice including code reviews, shared patterns, documentation, onboarding etc. Own the AI-native development practice. Use AI coding tools as a native part of how you write, review, and migrate data models (not as a demonstration, but as your actual workflow). Evaluate what works in a governed data engineering context, what introduces risk, and shape how the Analytics Engineering community adopts it.Lead responsible AI-augmented migration work using AI tooling to accelerate data modelling and migrations across the Analytics Warehouse and Operational Data Platform.Design data models and metric definitions in a documented and structured manner enabling reliable consumption by AI services without human intervention.Engage upstream with backend and product engineers to drive data contract discipline and schema ownership upstream, rather than managing inconsistency downstream.Contribute to Data & AI Products for external customer-facing analytics where data modelling patterns and conversational analytics needs and capabilities grow.Deep BigQuery and SQL expertise, including performance, cost considerations, and the architectural challenges of complex analytical domains.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.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.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.The Intelligence teams have strong AEs doing that. 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.The semantic layer and modelling standards you set have downstream consequences across the whole function and in AI features customers use.Contributed to the semantic layer tooling decision and begun onboarding the Analytics Engineers who'll build to it by writing documentation, running live sessions, reviewing PRs etc.Established a visible presence in the AE community of practice and become someone whose feedback in code reviews and whose opinions on architecture are listened to because they've been demonstrated, not just stated.Run your own workflow on AI coding tooling to the point where you can say clearly what the function should adopt, what it should not, and why.A 30-minute chat with our Talent Partner to discuss the role and your background.A technical test you'll be taking through an external platform to showcase your grasp of analytics engineering fundamentals.With this in mind, we want to help you understand what we really care about when reviewing your application:We receive a lot of CVs, and many of them are AI-generated. We love seeing people leverage AI—it's a big focus for us internally too—but without human intervention, these CVs can sometimes become generic and fail to show a candidate in the best light. What we're really looking for is the specific details of real impact that only you—not AI—know from your previous experience. A top tip from us is to use the "Achieved X, as measured by Y, by doing Z" formula (credit: A final note: including links to your previous companies' websites is a huge help and allows us to truly understand your background.Since it's our company language, please submit your application in English. Our talent team reads every single application to ensure the process is fair. Diversity drives us. 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.
Staff Engineer in London
