Data Analyst — Los Angeles, CA

About The Role

The role turns raw operational data into the metrics and insights that drive company decision-making - owning everything from data exploration to executive dashboards and self-serve analytics infrastructure.

The position sits at the intersection of analytics and engineering: writing production-quality SQL, partnering with data engineers on warehouse modeling, and translating ambiguous business questions into rigorous, reproducible analyses.

Key Responsibilities

• Own end-to-end business analytics: define KPIs, build metric definitions, and maintain alignment across product, growth, and operations stakeholders

• Write complex, performant SQL against a cloud data warehouse (Snowflake, BigQuery, or Redshift) to answer questions spanning user behavior, retention, revenue, and funnel performance

• Build and maintain dashboards in Looker, Tableau, or Mode that leadership and cross-functional teams rely on daily

• Develop dbt models and contribute to dimensional data modeling, working alongside data engineers on warehouse architecture and documentation

• Design and execute deep-dive analyses - cohort retention, A/B test evaluation, unit economics - using Python or R, and present findings to senior stakeholders with clear recommendations

• Improve data reliability by monitoring dashboards and pipelines, investigating anomalies, and partnering with data engineering on root-cause fixes

• Empower self-serve analytics across the org: document metrics, build reusable datasets, and train non-technical users on tooling

What We Are Looking For

• 3-6 years of experience in data analytics, analytics engineering, or a closely related role at a product-driven company

• Expert-level SQL - complex joins, window functions, query optimization - and proven experience with a modern cloud data warehouse

• Hands-on experience with at least one BI platform (Looker, Tableau, Mode, or Power BI) and dashboard design for executive audiences

• Proficiency in Python or R for analysis: pandas, statistical testing, and data manipulation at scale

• Experience with dbt and dimensional modeling; strong grasp of data warehousing concepts like star schemas and slowly changing dimensions

• Track record of communicating analytical findings to non-technical stakeholders and influencing product or business decisions

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

• Bonus: Experience with experimentation platforms (e.g., Statsig, Optimizely), PPenj84/Dagster orchestration, or building metrics layers in Snowflake