About the role

Staff Data Analyst at Supabase helping to build analytical foundations for sales while working remotely. Collaborating with teams to connect data systems and provide insights for business strategy.

Responsibilities

Build and own the canonical data products that Sales, CS, and GTM Ops rely on -- ARR, pipeline, account health, expansion signals, and renewal tracking
Define and document GTM metric standards so the whole org is working from consistent definitions
Build and maintain the underlying data models that power GTM reporting and analysis
Serve as the dedicated analytical resource for Sales, Customer Success, and GTM Ops leadership
Translate business questions into clean analytical specs and deliver decision-ready findings
Build self-service tools and context layers that enable GTM teams to answer their own questions -- the goal is scale, not dependency
Own the GTM analytics function end-to-end, from data model to business strategy
Connect the dots between CRM, product usage, and billing data to give GTM teams a complete picture of the customer
Identify gaps and inconsistencies across systems and work cross-functionally to resolve them
Use AI tools to accelerate analysis, increase output, and go deeper than a traditional analyst workflow allows
Help establish AI-forward ways of working within the Data Intelligence team and model that approach for the broader GTM org

Have 6+ years in analytics with meaningful time directly supporting Sales, Customer Success, or GTM Ops
Have built data models and BI layers from scratch, not just consumed them -- you understand the full stack from raw source data to business-facing output
Are comfortable working with CRM, product usage, and billing data and know how to connect them into a coherent picture of the customer
Have experience in a PLG or usage-based business and understand how self-serve signals translate into sales motion
Think in terms of self-service and scale -- your instinct is to build tools that make GTM teams independently capable, not to become a bottleneck
Can hold both the technical and strategic context at once -- you're as comfortable in a data model as you are in a conversation about territory design or expansion strategy
Are AI-forward in how you work -- you use LLMs and AI tooling habitually and have a clear point of view on how it changes what an analyst can do
Communicate insights clearly to non-technical stakeholders and know how to make data land in a business conversation
Thrive in async, autonomous environments and are energized by building something from the ground up