Business Data Analyst

About The Role

The role sits at the intersection of analytics and engineering - translating raw operational data into trustworthy metrics, dashboards, and insights that leadership and product teams use to make decisions weekly.

You will work within a modern data stack alongside data engineers and analytics engineers, owning the analytical layer: semantic models, KPI definitions, and experiments that directly shape product and go-to-market strategy.

Key Responsibilities

• Build and maintain core business dashboards in Looker (or Tableau) covering growth, retention, and operational efficiency metrics
• Write performant SQL models in Snowflake/BigQuery using dbt, ensuring version control, testing, and documentation for the semantic layer
• Design and analyze A/B tests and quasi-experiments, partnering with data scientists on experiment design and statistical rigor
• Partner with product, marketing, and finance stakeholders to define KPIs, diagnose metric anomalies, and deliver actionable insights
• Develop Python-based analyses (pandas, Jupyter) for churn modeling, cohort analysis, and funnel optimization
• Identify and fix data quality issues upstream in ELT pipelines (Airflow/Fivetran), collaborating with data engineers on root cause resolution
• Automate recurring reporting workflows, eliminating manual processes and improving self-serve analytics for the organization

What We Are Looking For

• 3–6 years of experience in data analytics, business intelligence, or analytics engineering, ideally at a high-growth technology company
• Expert-level SQL and hands-on experience with dbt or similar transformation frameworks in a cloud warehouse (Snowflake, BigQuery, Redshift)
• Strong Python skills for data analysis and automation (pandas, matplotlib or equivalent)
• Demonstrated experience building dashboards and semantic models in Looker, Tableau, or Power BI
• Solid foundation in statistics: hypothesis testing, experiment design, regression, and common analytical pitfalls
• Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experience
• Bonus: experience with data catalog tools (Atlan, Monte Carlo), reverse ETL workflows, or owning metric layers in a data mesh environment