Staff Analytics Engineer

• Own the semantic layer: define what a metric definition is, how it's structured, where it lives, and how it's enforced. The goal is one canonical definition of Monthly recurring revenue (MRR), churn, transaction, customer - used by analysts, BI tools, and AI tools without divergence. You make that real, not aspirational.

• 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. The goal is to create a social infrastructure that makes standards stick.

• 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 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.

This role is a good fit if:

• 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.

This role is not a good fit if

• 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.

By the end of your first six months, you'll have:

• Mapped the current state of modelling standards and the semantic layer across the function (where definitions are consistent, where they diverge, and what the highest-impact gaps are) to draft a plan the function has bought into.

• 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.

We want to ensure you are set-up for success and understand what will be expected of you. If your application is successful, our interview process is as follows:

• Intro call: A 30-minute chat with our Talent Partner to discuss the role and your background.

• Core skills test: A technical test you'll be taking through an external platform to showcase your grasp of analytics engineering fundamentals.

• Hiring Manager interview: A 60-minute conversation-based interview aimed at deep diving into your previous experience and learning more about the team.

• Pleo Challenge: A live practical and technical interview designed to assess your problem solving skills as well as your technical expertise.

• Final interview: A 45-minutes interview with one of our senior leaders, focusing on assessing behavioural skills and values alignment.

Transparency is important to us so we also wanted to share some insights about what we're looking for in applications to ensure you can set yourself up for success! 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: Laszlo Bock, ~2014) to give a really clear picture of what you've worked on. A final note: including links to your previous companies' websites is a huge help and allows us to truly understand your background.

• Every single application we receive is reviewed by a human (yes, hundreds of them) because we believe that candidates' efforts should be matched by an equal level of human care. This means that we expect a similar level of attention put into your application. Read and answer the application questions carefully, they make a huge difference in our decision-making process.

• This is a Staff level role at Pleo. This means it's a very senior position where you need to demonstrate experience leading technical initiatives that can have organisation-wide or cross-team impact for a tech business of 500-1000+ people. Prior experience owning a semantic layer is also a non-negotiable. Make sure your application reflects this.