Business Data Analyst

About The Role

The Business Data Analyst sits at the intersection of data infrastructure and business decision-making, turning complex pipelines and warehouse data into metrics, dashboards, and insights that leadership actually uses to steer the company.

The role works closely with analytics engineers to model data correctly at the source, and directly with product, ops, and finance stakeholders to define KPIs, investigate anomalies, and quantify the impact of business initiatives.

Key Responsibilities

Build and maintain executive and operational dashboards in Looker (or Tableau), owning metric definitions and ensuring consistency across teams Write performant SQL against cloud data warehouses (Snowflake, BigQuery, or Redshift) to answer ambiguous business questions with rigorous, reproducible analysis Partner with analytics engineers to develop and maintain dbt models and semantic layers, ensuring data quality and test coverage on core business tables Define, implement, and monitor KPI frameworks across product, growth, and operations, including funnel, retention, and cohort analyses Run deep-dive investigations — churn drivers, margin shifts, usage anomalies — and deliver clear written and visual recommendations to senior stakeholders Perform statistical analyses and A/B test evaluations in Python or R, translating results into business language and actionable next steps Serve as the go-to partner for product and finance teams during planning cycles, providing models and forecasts grounded in reliable data What We Are Looking For 3–6 years of experience in business analytics, data analytics, or a comparable role at a product-led technology company Expert-level SQL, including window functions, CTEs, and query optimization against large-scale cloud data warehouses Hands-on experience with at least one BI platform (Looker, Tableau, or Power BI) and ownership of dashboards used by executives or senior leaders Working proficiency in Python or R for analysis, modeling, and automation Familiarity with modern data stack tooling (dbt, Fivetran, Airflow) and experience collaborating with analytics or data engineering teams Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experience Bonus: Experience with experimentation platforms, revenue/margin analytics, LookML development, or building semantic layers; familiarity with Snowflake and dbt in production