Business Data Analyst

About The Role

The Business Data Analyst sits at the intersection of data infrastructure and business decision-making: translating raw pipeline outputs into reliable metrics, dashboards, and analyses that leadership actually uses to steer the company.

You will work closely with data engineers, product managers, and go-to-market teams, owning the semantic layer and reporting stack while identifying where data quality, instrumentation, or analysis needs improvement.

Key Responsibilities

• Build and maintain core business metrics (retention, funnel conversion, unit economics) using SQL against the warehouse (Snowflake, BigQuery, or Redshift)
• Develop and own dashboards in Looker, Tableau, or dbt-generated reporting layers used by product, sales, and executive teams
• Partner with data engineers to design data models and dbt transformations that make analytics self-serve and trustworthy
• Run deep-dive analyses on user behavior, revenue drivers, and operational efficiency, communicating findings with clear written narratives and visualizations
• Partner with product managers to define experiment metrics, review A/B test results, and flag statistical red flags
• Audit and improve data quality: build anomaly detection checks, resolve metric discrepancies, and document metric definitions
• Advocate for and drive adoption of self-serve analytics tooling across non-technical stakeholders

What We Are Looking For

• 3–6 years of experience in data analytics, business intelligence, or analytics engineering at a product-driven technology company
• Expert-level SQL: window functions, CTEs, query optimization on large datasets
• Hands-on experience with a modern data stack: dbt, Snowflake/BigQuery, and a BI tool (Looker, Tableau, or Mode)
• Strong proficiency in Python (pandas) or R for ad hoc analysis and statistical work
• A track record of translating ambiguous business questions into concrete, measurable analyses
• Bachelor's degree in a quantitative field (statistics, economics, CS, engineering) or equivalent practical experience
• Bonus: experience with A/B testing frameworks, Python data visualization (Plotly, Streamlit), or building metrics layers / semantic models