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Fullstack Data Analyst; Commercial Analytics

Job in Greater London, London, Greater London, W1B, England, UK
Listing for: Pleo
Full Time position
Listed on 2026-09-27
Job specializations:
  • IT/Tech
    Business Intelligence, Data Analyst
Salary/Wage Range or Industry Benchmark: 90000 - 140000 GBP Yearly GBP 90000.00 140000.00 YEAR
Job Description & How to Apply Below
Position: Staff Fullstack Data Analyst (Commercial Analytics)
Location: Greater 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:

About the role

Growth Intelligence owns the commercial data layer that powers Pleo's GTM engine — acquisition, retention, propensity modelling, and customer health. The team supports Rev Ops, Customer Experience, Customer Success, and senior commercial leadership with the data and models they need to make faster, smarter decisions.

As a Staff Full Stack Data Analyst, you are the senior technical voice for commercial analytics  set the architectural direction for Growth Intelligence's analytics layer, define what good looks like for the team, and shape how GTM data is structured, governed, and consumed by people and increasingly by AI tools.

You bring technical depth, commercial instinct, and organisational reach in equal measure. You move between architecture reviews and executive presentations, between defining metric contracts and mentoring the analysts who implement them. You don't just answer questions - you identify the right ones, and you make sure the answers compound over time.

Who you'll work with

You will report to the Senior Manager of the Growth Intelligence team. You will partner closely with senior commercial leadership, Customer Experience, Sales, and Customer Success teams; the onboarding and self-serve product teams;
Data Scientists and Analytics Engineers across the broader data organization; and Data Services & Governance on standards and semantic layer ownership. You will be a visible, senior presence across the entire data community at Pleo.

What you'll be doing
  • Own the architectural direction of the analytics layer for customer acquisition, onboarding, growth, and retention. You'll be making systemic decisions about how models are structured, layered, and governed, not just what gets built next.

  • Identify and resolve upstream data quality issues at source, treating quality gaps as governance and contract problems, articulating the business risk they create, and driving the org-level changes needed to fix them durably.

  • Define canonical GTM metric definitions in the semantic layer, in partnership with Data Services & Governance while ensuring Rev Ops, CS, CX, BI tools, and AI tooling are working from the same source of truth, and holding that standard over time.

  • Partner with senior commercial leadership to shape the questions worth answering.

  • Lead in-depth analysis of customer behaviour, commercial performance, and retention patterns.

  • Design and govern the team's approach to experimentation ensuring statistical rigour, scalable methodology, and clear criteria for what constitutes a meaningful result.

  • Build analytics explicitly…

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