Business Data Analyst

About The Role The role sits at the intersection of data engineering and business analytics - turning raw pipeline data into decision-grade metrics, dashboards, and analyses that leadership and product teams actually use. The position owns the semantic layer of a growing data platform: defining metrics, maintaining dbt models, and partnering with stakeholders across product, growth, and finance to answer questions the warehouse can't answer yet. Key Responsibilities Build and maintain curated data models in Snowflake using dbt, ensuring consistent metric definitions across BI tools and downstream consumersDesign and ship dashboards in Looker (or Tableau) for executives, product managers, and marketing teams - with clear documentation, not one-off reportsPartner with data engineers to improve pipeline reliability and data quality; define tests, monitor freshness, and triage broken SLAsRun deep-dive analyses on retention, funnel conversion, unit economics, and experiment results using SQL and PythonTranslate ambiguous business questions into rigorous analyses with clear methodology, quantified confidence, and actionable recommendationsDefine and document the metrics catalog, acting as the arbiter of 'what does this number actually mean' across the organizationSupport A/B test design and evaluation, working with product and engineering teams on experiment readouts What We Are Looking For 3-6 years of experience in analytics, business intelligence, or analytics engineering at a product-led or data-driven companyExpert-level SQL - complex window functions, CTEs, query optimization - and comfort working directly in large warehouses (Snowflake, BigQuery, or Redshift)Hands-on experience with dbt (models, tests, documentation) or an equivalent transformation frameworkStrong dashboarding skills in Looker, Tableau, or Power BI, including experience building the semantic models behind themWorking knowledge of Python (pandas) for ad hoc analysis and automationBachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experienceBonus: Experience with experimentation platforms and statistical testing (t-tests, Bayesian methods), event analytics tools (Amplitude, Mixpanel), or exposure to reverse ETL and data activation tools (Hightouch, Census)