Business Data Analyst

About The Role

The role bridges the gap between raw data infrastructure and executive decision-making. As a Business Data Analyst, you will design and build analytical data models, clean complex transactional datasets, and construct intuitive BI dashboards that provide actionable insights across product, operations, and finance teams.

This position sits within a centralized analytics team, working closely with data engineering to structure downstream data warehouses and with business leaders to translate complex metrics into clear operational strategies.

Key Responsibilities

• Write complex SQL queries to extract, transform, and analyze multi-source transactional and behavioral datasets within Snowflake
• Design, build, and maintain production-ready dashboards in Tableau or Looker that track company-wide Key Performance Indicators (KPIs)
• Partner with data engineers to define requirements for dbt models, helping optimize the core data warehouse architecture for downstream reporting
• Conduct deep-dive statistical analyses to identify trends, anomalies, and optimization opportunities within user acquisition and retention funnels
• Establish data governance and validation standards to ensure reporting accuracy, consistency, and alignment across all business units
• Translate complex analytical findings into structured, non-technical presentations for leadership and external stakeholders

What We Are Looking For

• 3–6 years of experience in data analytics, business intelligence, or a highly quantitative analytical role
• Expert-level SQL skills with proven experience writing, optimizing, and debugging complex queries against large-scale cloud data warehouses
• Hands-on proficiency with BI tools, specifically Looker (LookML) or Tableau, including dashboard design and data modeling
• Familiarity with modern data stack tooling such as dbt, git/GitHub, and orchestration workflows
• Strong communication skills with a track record of translating complex metrics into clear, actionable business recommendations
• Bachelor's degree in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field
• Bonus: Proficiency in Python or R for exploratory data analysis, or experience working with SaaS product metrics (LTV, CAC, Churn)