Senior Fullstack Data Analyst (Commercial Analytics)

• 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.

• 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.

• 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.

By the end of your first six months, you'll have built things that GTM teams are actively relying on.

• Growth Intelligence's core analytics layer: activation funnels, customer health reporting, retention signals will be in better shape than when you arrived. You'll know which models and dashboards matter most, where the gaps are, and have fixed at least some of them in ways that reduce manual work for RevOps, CS, and CX.

• You'll have contributed real metric definitions to the semantic layer, agreed with Data Services & Governance on what canonical GTM metrics actually are, and made that stick downstream. Analysts and AI tools will be getting consistent answers rather than each team maintaining their own version.

• You'll have a genuine working relationship with the commercial stakeholders you serve - RevOps, Customer Experience, Customer Success - and they'll have a clear sense of what to bring to you and what to expect back.

• And you'll have shipped at least one piece of analysis or tooling that changed a commercial decision or enabled something that wasn't possible before, not just improved the plumbing, but unlocked actual use.

• Your own Pleo card (no more out-of-pocket spending!)

• Lunch is on us for your work days - enjoy catered meals or receive a lunch allowance based on your local office

• Comprehensive private healthcare - depending on your location, coverage options include Vitality, Alan or Médis

• 25 days of annual leave + public holidays (additional days may apply in select countries based on local standards).

• For our Team, we offer both hybrid and fully remote working options

• We use MyndUp to give our employees access to free mental health and well-being support with great success so far

• Paid parental leave - we want to make sure that we're supportive of families and help you feel that you don't have to compromise your family due to work

We want to ensure you are set-up for success and understand what will be expected of you. If your application is successful, our interview process is as follows:

• Intro call: A 30-minute chat with our Talent Partner to discuss the role and your background.

• An async core skills test assessing your SQL fundamentals

• Hiring Manager interview: A 60-minute experience-based interview to deep dive into your domain knowledge and technical experience.

• Pleo Challenge: A live technical case interview designed to assess your technical expertise around data modeling, manipulation and insights generation.

Transparency is important to us so we also wanted to share some insights about what we're looking for in applications to ensure you can set yourself up for success! With this in mind, we want to help you understand what we really care about when reviewing your application:

• We receive a lot of CVs, and many of them are AI-generated. We love seeing people leverage AI - it's a big focus for us internally too- but without human intervention, these CVs can sometimes become generic and fail to show a candidate in the best light. What we're really looking for is the specific details of real impact that only you - not AI - know from your previous experience. A top tip from us is to use the "Achieved X, as measured by Y, by doing Z" formula (credit: Laszlo Bock, ~2014) to give a really clear picture of what you've worked on. A final note: including links to your previous companies' websites is a huge help and allows us to truly understand your background.

• Every single application we receive is reviewed by a human (yes, hundreds of them) because we believe that candidates' efforts should be matched by an equal level of human care. This means that we expect a similar level of attention put into your application. Read and answer the application questions carefully, they make a huge difference in our decision-making process.