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About Pleo
Messy spend management is tricky business. And tedious processes are a lose-lose situation for all involved, not just finance. At Pleo, we're changing that. 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’.
The word ‘Pleo’ actually means ‘more than you’d expect’, and living by that mantra has been the secret to our success over the last 10 years.
Now, we’re at a pivotal moment in our journey; every move we make has a direct impact on our 40,000+ customers, our business, and our collective success. We need people who take pride in uncovering customer needs, who turn complex problems into simple solutions, challenge the way things are done (respectfully), and always aim high. With great ambitions driving us forward, we can’t say we’ve got this whole thing figured out. And frankly, that’s half the fun! What we can say is that we’re a driven, progressive, and, importantly, a kind bunch of 850+ people from over 100 nationalities, all committed to delivering the future of business spending, together.
Please note: applications are open until 2nd September 2026 09.00 CEST. We will not review any application before the closing date. Please do not rush and use this time to submit a high-quality application!
About the role
This 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 works 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 semantic layer is still in a very early stage. Tooling selection is live, and this role has a strong voice in it. 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 includes: GCP, BigQuery, dbt Core, Airflow, SQL, Python, Claude Code, GitHub Copilot.
Who you'll work with
- You'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.
What you'll be doing
- 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.
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What you bring
- 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.
Why this role is a good fit for you
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.
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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.
Your first 6 months
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.
The interview process
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-minute 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
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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