About The Role
The Business Data Analyst owns the analytical layer between data infrastructure and decision-making — turning warehouse data into dashboards, metrics, and insights that drive product, growth, and operations decisions. The role works with dbt models, SQL-first transformations, and BI tooling in a data environment built for scale.
This is a high-visibility position: the analyst partners directly with product managers, finance, and engineering leadership, and the quality of their work shapes how the business measures itself. The team values rigorous methodology over quick answers.
Key Responsibilities
Design, build, and maintain executive and operational dashboards in Looker or Tableau, serving stakeholders across product, marketing, and finance
Develop and document dbt models and semantic layers, owning metric definitions end-to-end from raw events to governed business metrics
Run deep-dive analyses on user behavior, retention, funnel conversion, and unit economics using SQL and Python
Partner with product managers to define success metrics, design A/B test frameworks, and interpret experiment results with statistical rigor
Identify data quality issues at the source — writing dbt tests and monitoring checks to catch anomalies before they reach stakeholders
Translate ambiguous business questions into structured analytical frameworks, communicating findings through clear narratives and actionable recommendations
Contribute to self-serve analytics enablement: build data documentation, train stakeholders, and reduce ad-hoc request load over time
What We Are Looking For
3–6 years of experience in business analytics, data analytics, or analytics engineering at a product-driven technology company
Expert-level SQL — complex window functions, CTEs, query optimization — demonstrated daily in real workflows
Hands-on experience with a modern data stack: Snowflake/BigQuery/Redshift, dbt, and at least one BI tool (Looker, Tableau, or Power BI)
Working proficiency in Python or R for analysis, including pandas and statistical libraries
Strong foundation in statistics and experimentation: hypothesis testing, significance, sample sizing, and A/B test interpretation
Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experience
Bonus: experience with event-level analytics tools (Segment, Amplitude, Mixpanel), exposure to data cataloging or semantic layer tools (Cube, Atlan), and a track record of automating recurring reporting with Airflow or similar orchestrators