Data Analyst

About The Role
The role sits at the center of the data organization, turning raw pipeline output into metrics, dashboards, and analyses that drive product and business decisions. Day-to-day work spans SQL modeling in the warehouse, building reliable semantic layers, and partnering directly with product and growth teams on experimentation and KPI definition.
The team values analysts who treat data as an engineering discipline: version-controlled transformations, tested data models, and clear documentation matter as much as insight generation. The role reports to the Head of Data Analytics and has real ownership over how core business metrics are defined and measured.
Key Responsibilities

• Build and maintain dbt models in Snowflake, defining core metrics and ensuring consistency across downstream dashboards and reporting layers
• Design and ship dashboards in Looker (or Tableau) that product, growth, and leadership teams use daily for decision-making
• Partner with data engineers to improve pipeline reliability, data contracts, and freshness SLAs for source data feeding analytical models
• Run in-depth analyses on activation, retention, and revenue trends; clearly communicate findings and recommendations to stakeholders
• Design and analyze A/B tests, including experiment sizing, statistical significance checks, and post-launch readouts
• Define and document KPI logic in a semantic layer, acting as the source of truth for metric definitions across the company
• Proactively identify data quality issues, root-cause anomalies, and build tests in dbt to catch regressions before stakeholders do
  

What We Are Looking For

• 3+ years of experience in data analytics, analytics engineering, or a closely related role at a product-driven technology company
• Expert-level SQL, including window functions, CTEs, and query optimization against large warehouse datasets
• Hands-on experience with dbt (or equivalent transformation tooling) and a modern data warehouse such as Snowflake, BigQuery, or Redshift
• Proficiency in Python or R for ad hoc analysis, statistical testing, and light automation
• Experience building executive-facing dashboards in Looker, Tableau, or Power BI, with strong data visualization fundamentals
• Solid grasp of experiment design and statistical concepts: hypothesis testing, confidence intervals, sample sizing
• Bonus: experience with LookML, event analytics tools (Amplitude, Mixpanel), reverse ETL tools (Hightouch, Census), or Airflow