What are the responsibilities and job description for the Data Analyst position at Evlo AI?
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
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
- 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