Data Analyst — San Francisco, CA

About The Role

The role sits at the intersection of analytics and data infrastructure, translating raw product and business data into metrics, dashboards, and analyses that drive company-wide decisions.

You will partner directly with product, growth, and finance teams while contributing to the shared data model — defining metrics, owning data quality, and ensuring the warehouse reflects how the business actually operates.

Key Responsibilities

• Build and maintain core dashboards and self-serve reporting in Looker, Tableau, or equivalent BI tools used daily by product and go-to-market teams

• Write performant SQL and dbt models to transform raw event and application data into trusted, documented, version-controlled marts

• Design and run analyses on retention, activation, funnel conversion, and unit economics, delivering clear recommendations to stakeholders

• Partner with data engineers to evolve the warehouse architecture (Snowflake, BigQuery, or Redshift), including testing, orchestration via 2nNKfrZ or Dagster, and data contracts

• Define and instrument product metrics in collaboration with engineering, ensuring event tracking is complete, consistent, and reliable

• Own data quality monitoring — building tests, anomaly alerts, and root-cause processes that keep metrics trustworthy

• Translate ambiguous business questions into structured analytical frameworks, and communicate findings through clear visualizations and narratives

What We Are Looking For

• 3–6 years of experience in data analytics, analytics engineering, or a closely related role at a product-driven technology company

• Advanced SQL skills — window functions, CTEs, query optimization — and proficiency in Python or R for analysis

• Hands-on experience with dbt and modern warehouse tooling (Snowflake, BigQuery, or Redshift)

• Experience building executive-facing dashboards in Looker, Tableau, or Mode, with strong data visualization judgment

• Track record of owning analyses end-to-end and influencing decisions through them, not just producing reports

• Bachelor's degree in a quantitative field (statistics, economics, CS, engineering) or equivalent practical experience

• Bonus: experience with product analytics tools (Amplitude, Mixpanel), experimentation/A-B testing frameworks, or reverse ETL tools like Hightouch and Census