Staff Analytics Engineer in London

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 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 semantic layer is still in 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 will 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.
• 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.
• 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.
• Contribute to Data & AI Products for external customer-facing analytics.

What you bring

• Deep demonstrated expertise in dbt and modelling practices.
• 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).
• AI-native development practice in a data engineering context.
• A track record of setting standards across teams you don't directly manage.
• Understanding of what LLMs and agentic tools need from a data layer.
• Proven experience engaging credibly with backend engineers on data contracts and with senior stakeholders.

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.
• You genuinely enjoy growing a community of practice through influence rather than authority.
• You find the