Senior Full Stack Data Analyst at Pleo

Messy spend management is tricky business. At Pleo, we're changing that by building spend solutions that make managing money seamless, empowering, and effective for finance teams and employees alike. Our vision is to help all businesses ‘go beyond.’

The word ‘Pleo’ means ‘more than you’d expect,’ and this mantra has driven our success over the last 10 years. Now, we’re at a pivotal moment, impacting our 40,000+ customers and our collective success.

About the Role

Growth Intelligence owns the commercial data layer that powers Pleo's GTM engine, supporting RevOps, Customer Experience, Customer Success, and senior commercial leadership with data and models for faster, smarter decisions.

As a Senior Full Stack Data Analyst, you will:

• Build and maintain the analytics layer for customer acquisition, onboarding, growth, and retention, including propensity models, growth dashboards, activation funnels, and GTM reporting.
• 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 behavior, commercial performance, and retention patterns to surface trends, risks, and growth opportunities.
• Support onboarding and self-serve product teams with data on activation, engagement, and friction, translating product behavior into insights that improve the customer journey.
• Design and analyze experiments to test growth hypotheses and optimize key commercial metrics within Pleo's experimentation methodology.
• Build analytics explicitly designed for self-serve and AI access.
• Contribute metric definitions and domain knowledge to the semantic layer, ensuring 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’ll Work With

You will report to the Senior Manager of the Growth Intelligence team and partner primarily with Customer Experience, Sales, and Success teams. You will also work closely with onboarding and self-serve product teams, as well as colleagues across Growth Intelligence and the broader data community, including Data Scientists and Analytics Engineers. Technologies you’ll leverage include 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.
• Partner with teams to understand data needs and deliver actionable dashboards and analyses.
• Conduct in-depth analysis of customer behavior and commercial performance.
• Support product teams with data-driven insights to improve the customer journey.
• Design and analyze experiments to optimize commercial metrics.
• Build analytics for self-serve and AI access.
• Contribute to metric definitions and semantic layer consistency.
• Collaborate with data colleagues to share knowledge and maintain standards.

What You Bring

• Proven experience working across insights generation and analytics engineering in a SaaS environment (fintech is a plus).
• Solid proficiency with Python, SQL, and dbt; you write clean, tested models and understand layered architecture.
• BigQuery experience, including complex transformations and performance considerations.
• Proficiency with BI tools such as Looker and LookML.
• A commercial mindset understanding GTM metrics, customer lifecycle stages, and revenue outcomes.
• Strong data visualization skills to build dashboards that GTM stakeholders use.
• Understanding of how analytics outputs serve AI tools and self-serve analytics.
• Comfort with Git-based workflows and CI/CD practices for analytics code.
• Strong ownership and delivery discipline to manage multiple projects with attention to detail.
• Solid understanding of data contracts, schema agreements, ownership, and SLAs.

This Role Is Not a Good Fit If

• You prefer to specialize deeply in one area rather than wear multiple hats.
• You are not comfortable engaging directly with demanding commercial stakeholders.
• You need clean problem definitions before starting.

Your First 6 Months

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

• Improved Growth Intelligence's core analytics layer, including activation funnels, customer health reporting, and retention signals.
• Contributed real metric definitions to the semantic layer, ensuring consistency across analysts and AI tools.
• Established working relationships with commercial stakeholders.
• Shipped at least one piece of analysis or tooling that changed a commercial decision.