Staff Analytics Engineer

United States Digital Space LLC; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

PleoMessy is seeking a senior Analytics Engineer to own the semantic layer and analytics standards across the company. You’ll define metric definitions, govern dbt architecture, and lead AI-native development across the analytics stack.

You’ll collaborate with Data Infra, MLOps, and Intelligence teams to align data contracts and ensure scalable, AI-ready data products for internal and external use.

Qualifications

• Deep demonstrated expertise in dbt and modelling practices.
• Strong BigQuery and SQL expertise.
• Experience owning a semantic layer (LookML, dbt MetricFlow, or equivalent).

Responsibilities

• Own the semantic layer and define canonical metric definitions (MRR, churn, etc.).
• Set data modelling standards across domains and levels of complexity.
• Build and run analytics engineering communities of practice.
• Lead AI-native data engineering practices and govern AI tooling adoption.
• Collaborate with backend engineers and product teams on data contracts.

Skills

dbt

BigQuery

SQL

semantic layer

LookML

AI-native development

data contracts

stakeholder management

Tools

dbt

LookML

Copilot

Job description

About PleoMessy spend management is tricky business. And tedious processes are a lose-lose situation for all involved, not just finance. At the company, 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 ‘the company’ 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.

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 the company. 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.

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.

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

• 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.

Your first 6 months

• 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

• 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.

• the company 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.

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.