Senior Fullstack Data Analyst (Commercial analytics)

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.

About the role

Please note: applications will be open until Friday 2nd October 2026 at 08.00 CEST. We will not review any applications before the closing window so please don't rush and take the time you need to submit a high quality application.

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

As a Senior Full Stack Data Analyst, you sit at the intersection of commercial analytics and customer intelligence. You build end-to-end analytics solutions (retention models, growth analyses, activation funnels, and GTM dashboards) that directly influence how Pleo acquires, onboards, retains, and grows its customer base.

Who you'll work with

You will report to the Senior Manager of the Growth Intelligence team. You will partner primarily with Customer Experience, Sales, and Success teams. You will also work closely with the onboarding and self-serve product teams as well as colleagues across Growth Intelligence and the broader data community, including Data Scientists in your team and Analytics Engineers who own the modelling layer your analysis depends on.

For extra context, you'll also be leveraging technologies including SQL, dbt, BigQuery, Looker, Amplitude, HubSpot, Zuora, or Vitally.

What you'll be doing

Build and maintain the analytics layer for customer acquisition, onboarding, growth, and retention: propensity models, growth dashboards, activation funnels, and the commercial reporting GTM teams depend on day-to-day.

Partner with Customer Experience, Sales, and Success teams to understand their data needs and deliver dashboards and analyses that drive operational decisions.

Conduct in-depth analysis of customer behaviour, commercial performance, and retention patterns to surface trends, risks, and growth opportunities (owning the insight and narrative, not just the output).

Support the onboarding and self-serve product teams with data on activation, engagement, and friction, translating product behaviour into insights that improve the customer journey.

Design and analyse experiments to test growth hypotheses and optimise key commercial metrics within Pleo's experimentation methodology, ensuring statistical rigour.

Build analytics explicitly designed for self-serve and AI access

Contribute metric definitions and domain knowledge to the semantic layer, working with Data Services & Governance to ensure GTM metrics are consistently defined across BI tools and AI tooling.

Engage with data colleagues across Intelligence to share knowledge, maintain standards, and contribute to a culture of craft.

What you bring

Proven experience working across both insights generation and analytics engineering in a SaaS environment (fintech is a big plus).

Solid proficiency with Python, SQL and dbt: you write clean, tested models, understand layered architecture, can manipulate and analyse data to uncover insights.

BigQuery experience, including complex transformations and performance considerations.