Business Analytics Engineer

About the Role

Join Rad AI to build embedded analytics inside our reporting platform, providing radiology groups with self-service visibility into their data. You will act as a hybrid Data Engineer, Analyst, and Customer Partner, working directly with stakeholders like quality leads and IT teams to translate ambiguous needs (e.g., 'did our new templates help?') into rigorous metrics.

What You'll Do

• Data Foundation: Connect the data platform, troubleshoot quality issues per customer, and define governing contracts for multi-tenant environments.
• Discovery & Requirements: Lead conversations with radiology administrators to understand measurement goals beyond initial requests.
• Semantic Layer Design: Build governed metrics, joins, and reusable models using dbt/Snowflake practices that scale across customers rather than creating one-off reports.
• Statistical Rigor: Apply real statistical analysis to messy healthcare data (outliers, seasonality). Use confidence intervals, risk-adjustment, and exclusion notes where necessary. Direct AI agents to model datasets while verifying their output with your own critical judgment.
• Data Quality Ownership: Own end-to-end quality for customer-facing outputs, catching anomalies before the client sees them.

What You'll Bring

• Experience: 2–5 years in analytics engineering or data science with a hunger to learn. We value curiosity over tenure.
• Ai Fluency: Concrete examples of how you have used AI tools (in the last six months) to describe, model, document datasets, and verify outputs.
• Technical Skills: Strong SQL for complex optimization; fluency in working statistics (CIs, significance testing); experience with governed BI/semantic layers like Omni or Looker; comfort building reusable dbt/Snowflake models.
• Communication: Ability to explain confidence intervals and caveats clearly to non-technical clinicians and executives without dumbing down the substance.