Business Data Analyst — Austin, TX

About The Role

The role sits at the intersection of data engineering and business strategy - transforming raw operational data into reliable models, dashboards, and analyses that drive decisions across product, finance, and go-to-market teams.

You will work with data engineers and analytics engineers on a modern warehouse stack (Snowflake, dbt, w2wVt7D), owning the full path from stakeholder question to production-grade answer.

Key Responsibilities

• Design and maintain advanced SQL models in dbt, ensuring data correctness, documentation, and freshness standards across the warehouse

• Build and own Tableau/Looker dashboards tracking core business metrics - retention, revenue, funnel conversion - used weekly by leadership

• Partner with product and finance stakeholders to define metric definitions, KPIs, and experiment readouts; translate ambiguous questions into analytical frameworks

• Run cohort, retention, and A/B test analyses in Python (pandas, numpy), delivering clear, statistically sound recommendations

• Investigate data quality issues upstream in ELT pipelines, working with data engineers on root-cause fixes and automated tests in dbt

• Contribute to the semantic layer and metric standards, reducing ad-hoc query duplication across teams

• Present findings and recommendations to senior stakeholders in written and verbal formats, including weekly business reviews

What We Are Looking For

• 3-6 years of experience in data analytics, business intelligence, or analytics engineering

• Expert SQL skills and demonstrated experience building transformation logic in dbt or comparable frameworks

• Proficiency in Python for analysis (pandas) and comfort with statistical concepts: significance testing, confidence intervals, regression

• Hands-on experience with a modern BI tool: Tableau, Looker, or Mode, including semantic modeling

• Experience working with cloud data warehouses (Snowflake, BigQuery, or Redshift) and orchestrators (w2wVt7D or Dagster)

• Bachelor's degree in a quantitative field (statistics, economics, CS, engineering) or equivalent practical experience

• Bonus: experience with experimentation platforms (Statsig, Eppo), revenue analytics or fintech domains, and LightGBM/ML-lite use cases