Business Data Analyst

About The Role

The role sits at the intersection of data infrastructure and business decision-making - transforming raw event, transactional, and operational data into models and dashboards that leadership actually uses to steer the business.

You will partner directly with product, finance, and go-to-market teams to define metrics, build self-serve reporting, and investigate the 'why' behind business trends - working in a modern warehouse-centric stack (dbt, Snowflake, Python, BI tooling) alongside data engineers and analytics engineers.

Key Responsibilities

Build and maintain core business metrics and semantic layers in dbt, ensuring definitions are consistent, documented, and trusted across teams Develop and own dashboards in Looker, Tableau, or Mode for product, growth, and finance stakeholders - from wireframe to production Write advanced SQL and Python analyses to answer open-ended business questions: retention drivers, funnel conversion, unit economics, cohort behavior Design and run A/B test analyses, including experiment readouts, statistical significance assessments, and guardrail metric monitoring Partner with data engineers to improve pipeline reliability, data quality checks, and warehouse cost efficiency Build forecasting and anomaly detection models to surface revenue and engagement risks before they hit leadership dashboards Translate stakeholder requests into clear problem statements, prioritized backlogs, and documented analytical deliverables What We Are Looking For 3+ years of experience in business analytics, data analysis, or analytics engineering at a product-driven technology company Expert-level SQL - window functions, CTEs, query optimization on large warehouse datasets (Snowflake, BigQuery, or Redshift) Strong Python or R for analysis: pandas, statistical testing, and lightweight modeling Hands-on experience with dbt or a comparable transformation framework and modern BI tools (Looker, Tableau, or Mode) Track record of translating ambiguous business problems into rigorous analyses with clear, actionable recommendations Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experience Bonus: experience with experimentation platforms, cohort/LTV modeling, Airflow or Dagster, and stakeholder-facing communication at the executive level