Business Data Analyst

About The Role

The role sits at the intersection of data infrastructure and business decision-making: transforming raw product, marketing, and operational data into trustworthy models, metrics, and dashboards that leadership and cross-functional teams use daily.

The analyst works closely with analytics engineers and data engineers to own the semantic layer end-to-end — from dbt models and warehouse tables to executive-facing reporting — and is accountable for the accuracy and adoption of the metrics the business runs on.

Key Responsibilities

Design, build, and maintain core business metrics and dashboards in Tableau or Looker, serving product, marketing, and finance stakeholders Develop and version-control dimensional models and transformation logic in dbt on top of Snowflake, ensuring data freshness, tests, and documentation standards are met Partner with data engineers to debug pipeline failures, validate upstream data quality, and backfill historical datasets with SQL Lead self-serve analytics enablement: build semantic definitions, train stakeholders on tools, and reduce ad-hoc reporting requests over time Run deep-dive analyses on funnel conversion, retention, and unit economics using SQL and Python; translate findings into prioritized recommendations Define and instrument KPIs for A/B tests and feature launches in collaboration with product managers and experiment platforms Document metric definitions and business logic in a shared catalog to eliminate inconsistencies across reporting layers What We Are Looking For 3–6 years of experience in data analytics, business intelligence, or analytics engineering roles at product-driven companies Expert-level SQL: complex joins, window functions, CTEs, and query optimization against large cloud data warehouses (Snowflake, BigQuery, or Redshift) Hands-on experience with dbt and a strong understanding of dimensional modeling and modern data stack architecture Proficiency with a BI tool such as Tableau, Looker, or Power BI, including building performant dashboards for executive audiences Working knowledge of Python or R for analysis, plus familiarity with statistics fundamentals: hypothesis testing, significance, and confidence intervals Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experience Bonus: experience with Airflow or Dagster, event tracking tools (Segment, Amplitude), or supporting experimentation programs at scale