Senior Fullstack Data Analyst (Commercial Analytics)

Overview

In this role you will sit at the intersection of commercial analytics and customer intelligence, building end-to-end analytics solutions that shape how Pleo acquires, onboards, retains, and grows customers. You’ll work with cross-functional teams to deliver dashboards, models, and reports that inform GTM decisions and customer journeys. Your work will blend analytics generation with engineering of the data layer, enabling AI access and self-serve insights. You’ll operate in a fast-paced environment where rigorous experimentation and data storytelling drive business impact.

Responsibilities

• Build and maintain the analytics layer for acquisition, onboarding, growth, and retention (propensity models, growth dashboards, activation funnels, and GTM reports)
• Partner with Customer Experience, Sales, and Success to deliver dashboards and analyses that drive operational decisions
• Conduct in-depth analysis of customer behavior and performance to surface trends, risks, and growth opportunities
• Support onboarding and self-serve product teams with data on activation, engagement, and friction
• Design and analyze experiments to test growth hypotheses and optimize metrics within the experimentation framework
• Build analytics designed for self-serve and AI access; contribute metric definitions to the semantic layer
• Collaborate with Data Services & Governance to ensure consistent GTM metrics across BI and AI tooling
• Share knowledge across Intelligence and contribute to craft standards and practices

Key requirements

• Proven experience in insights generation and analytics engineering in a SaaS environment (fintech is a plus)
• Strong Python, SQL, and dbt skills with clean, tested models
• BigQuery experience with complex transformations
• BI tools proficiency (Looker and LookML)
• Commercial mindset with understanding of GTM metrics and revenue linkage
• Strong data visualization skills for actionable dashboards
• Experience designing analytics for AI and self-serve access; metrics consistency awareness
• Git-based workflows and CI/CD for analytics code
• Ownership and delivery discipline with multi-project management
• Understanding of data contracts, schema ownership, and SLAs

• stakeholder management
• ability to translate business needs into data solutions
• clear data storytelling and narrative ownership
• Python
• SQL
• dbt

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