Senior Pricing Data Scientist

From Fivetran’s founding until now, our mission has remained the same: to make access to data as simple and reliable as electricity. With Fivetran, customer data arrives in their warehouses, canonical and ready to query, with no engineering or maintenance required. We’re proud that more organizations continue to leverage our technology every day to become truly data-driven.

About Us

Fivetran and dbt Labs are bringing together two industry-leading companies with a shared mission: helping organizations unlock the full value of their data.

Together, we’re delivering the data infrastructure layer that helps organizations move, transform, and trust their data — from the moment data moves, through every transformation, to the context teams and AI systems rely on.

Fivetran helps organizations automate data movement across the systems, clouds, engines, and tools they rely on. dbt Labs pioneered analytics engineering, helping teams transform data into reliable, governed insights. Together, we support thousands of organizations as they build a trusted foundation for analytics, AI, and better business decisions.

As we bring our teams and technology together, we’re building on the strengths of both companies while continuing to deliver the products and experiences our customers know and trust. It’s an exciting time to join us: we’re creating a company with the scale, talent, and technology to help more organizations put their data to work with greater speed, confidence, and impact.

During this transition period, you may see references to both Fivetran and dbt Labs throughout our recruiting process as we integrate our teams, systems, and career sites.

About the Role

This is a unique role that sits at the intersection of economics, analytics, product, and strategy. You’ll combine rigorous quantitative analysis with customer research and commercial thinking to shape how Fivetran prices, packages, and monetizes its products.

What You’ll Do

• Analyze product usage, deal, financial, and billing data to model pricing, packaging, and monetization scenarios.

• Design frameworks to assess willingness to pay, customer value, and price realization across customer segments and product lines.