About The Role
The Data Analyst owns the analytical layer of a fast-scaling product and revenue organization — turning raw event streams, warehouse tables, and operational data into metrics leadership actually acts on.
The role sits between data engineering and business strategy: building reliable semantic layers, designing experiments, and shipping dashboards that answer questions before they're asked.
Key Responsibilities
Build and maintain SQL models in Snowflake/BigQuery (dbt preferred), ensuring data quality, documentation, and consistent metric definitions across teams
Design and ship dashboards in Looker or Tableau for product, growth, and finance stakeholders — with clear naming conventions and self-serve usability
Run A/B test design and analysis: experiment sizing, statistical significance testing, and post-launch readouts with clear business recommendations
Partner with data engineers to define ingestion requirements, improve pipeline reliability, and reduce time-to-insight on new data sources
Perform deep-dive analyses on retention, funnel conversion, unit economics, and user behavior using SQL and Python
Define and instrument KPIs with product managers, owning metric documentation and guarding against metric drift
Present findings to leadership with concise written narratives and clear visualizations — not just raw numbers
What We Are Looking For
3–6 years of experience in data analysis, analytics engineering, or a closely related role
Expert-level SQL — window functions, CTEs, query optimization — and strong Python for analysis (pandas, NumPy)
Hands-on experience with a modern warehouse (Snowflake, BigQuery, Redshift) and BI tools (Looker, Tableau, or Power BI)
Track record of owning analytics end-to-end: stakeholder intake, modeling, analysis, and executive communication
Solid statistics foundation: hypothesis testing, regression, experiment design, and knowing when not to overfit a conclusion
Bachelor's degree in a quantitative field or equivalent practical experience
Bonus: dbt experience, event analytics tooling (Amplitude, Mixpanel, Segment), Airflow exposure, or experience in a B2B SaaS environment