Senior Fullstack Data Analyst (Commercial Analytics)
Pleo
Job description
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. BI tool proficiency such as Looker and LookML Commercial mindset: you understand GTM metrics, customer lifecycle stages, and how data connects to revenue outcomes. Strong data visualisation skills: you build dashboards that GTM stakeholders actually use, not just ones that look good. Understanding of how analytics outputs serve AI tools and self-serve analytics as first-class consumers: you design for machine access as well as human access, and know why metric consistency matters as AI tooling scales. Comfort with Git-based workflows and CI/CD practices for analytics code. Strong ownership and delivery discipline: you manage multiple projects with close attention to detail and follow through. Solid understanding of data contracts: schema agreements, ownership, and SLAs between data producers and downstream consumers. This role is not a good fit if You prefer to specialise deeply in one area rather than wear multiple hats. This role requires you to move between analysis, analytics engineering, experimentation, and stakeholder management. You are not comfortable engaging directly with demanding commercial stakeholders and translating their needs into data solutions without a fully defined brief. You'll often need to shape the question as much as answer it. You need clean problem definitions before you can start. The commercial data environment here is genuinely complex. Definitions evolve, ownership is shared, and the right answer sometimes requires negotiation as much as analysis. Your first 6 months By the end of your first six months, you'll have built things that GTM te
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