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 consumers
• Design and ship dashboards in Looker (or Tableau) for executives, product managers, and marketing teams - with clear documentation, not one-off reports
• Partner with data engineers to improve pipeline reliability and data quality; define tests, monitor freshness, and triage broken SLAs
• Run deep-dive analyses on retention, funnel conversion, unit economics, and experiment results using SQL and Python
• Translate ambiguous business questions into rigorous analyses with clear methodology, quantified confidence, and actionable recommendations
• Define and document the metrics catalog, acting as the arbiter of 'what does this number actually mean' across the organization
• Support 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 company
• Expert-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 framework
• Strong dashboarding skills in Looker, Tableau, or Power BI, including experience building the semantic models behind them
• Working knowledge of Python (pandas) for ad hoc analysis and automation
• Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experience
• Bonus: 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)