Note: The job is a remote job and is open to candidates in USA. Rad AI is building embedded analytics in its radiology reporting platform to give radiology groups and health systems self-service visibility into their reporting data. The Business Analytics Engineer will partner with customers and internal teams to develop governed data models, reusable metrics, and dashboards, while ensuring data quality and explaining findings to clinical and administrative audiences.
Responsibilities
- Get the data foundation right. Connect the new data platform to the existing warehouse, troubleshoot and fix data quality issues per customer, and help define the data contract that doesn’t exist yet
- Gather and translate requirements with customers and internal partners. Join discovery conversations with radiology group administrators, quality/ops leads, and informatics teams to understand what they’re trying to measure and decide, not just what they initially ask for
- Design and build the semantic layer and dashboards that serve customer requirements: governed metrics, joins, and reusable models that hold up across customers rather than one-off reports per account
- Apply real statistical rigor to healthcare reporting data. Radiology data is messy and high-stakes: small sample sizes at some sites, outliers from edge-case studies, seasonality, case-mix differences across specialties and facility types. You'll need to know when a metric needs a confidence interval, a risk-adjustment, or an exclusion note, not just a number
- Use AI as a core part of your workflow. Direct AI agents to describe, model, and document data relationships, and verify what they produce
- Own data quality end-to-end for customer-facing outputs: catch and explain anomalies before a customer does, document known caveats (sample-size exclusions, data lag, definitional edge cases) directly in the deliverable
- Build durable data products, not one-off answers. Use dbt/Snowflake-style modeling practices so a metric built for one customer's dashboard is reusable for the next, instead of re-derived from scratch each time
- Partner cross-functionally with Product, Engineering, and Customer Success to prioritize which metrics and dashboards actually move the needle for customers versus which are just "nice to have."
- Explain data to non-data audiences. You’ll present findings to radiologists, hospital administrators, and executives who are expert clinicians but not data scientists. The ability to make a confidence interval or caveat meaningful to that audience is as important as computing it correctly
Skills
- 2–5 years in an analytics engineering, data science, or business/BI analyst role
- Strong SQL — comfortable writing and optimizing complex queries, not just filtering existing tables
- Working statistics fluency: confidence intervals, significance testing, sample-size considerations, handling outliers and skewed distributions, and the judgment to know when a metric needs a caveat versus when it's solid
- Experience with a modern governed BI/semantic layer tool (we use Omni; Looker, Sigma, ThoughtSpot, or similar is great) and a SQL/Python notebook or exploration environment (we use Hex; Mode or similar is fine)
- Comfortable in dbt/Snowflake-style modeling environments: building something once and having it hold up for the next ten customers
- Direct experience defining or maintaining a semantic layer (entity/metric definitions, joins, governed fields, e.g., LookML, dbt Semantic Layer, or an equivalent governed-BI modeling layer), not just consuming one someone else built
- Comfort with ambiguity. You're energized, not rattled, by undefined problems, messy data, and no existing playbook
- Demonstrated ability to run discovery with external stakeholders: asking good follow-up questions, pushing back on vague asks, and landing on a defensible metric definition
- Excellent written and verbal communication, able to present data findings to a non-technical, clinical audience without dumbing down the substance
- Ideally with some customer- or stakeholder-facing exposure (not purely internal reporting)
- Demonstrated AI fluency. Concrete examples from the last six months of how AI tools have meaningfully changed the way you work with data, including how you've used them to describe, model, or document datasets and how you check their output
- Healthcare, health IT, or other regulated-industry data experience is a strong plus (HIPAA awareness, understanding why "it depends on the payer/facility/specialty" is often the right answer)
- Familiarity with radiology-specific concepts (PACS, HL7/FHIR, turnaround time, RVU, template-driven reporting) is a bonus, not a requirement
- Experience building or maintaining a •customer-facing• (vs. internal-only) analytics product, i.e., you understand that a dashboard a paying customer sees is held to a different bar than an internal Slack chart
- Exposure to multi-tenant data governance (row-level security, per-customer data isolation) in an embedded analytics context
- Prior experience in a company selling into hospitals/health systems, understanding procurement and IT review cycles
Benefits
- Comprehensive Medical, Dental, Vision & Life insurance
- HSA (with employer match)
- FSA
- DCFSA
- 401(k)
- 11 Paid Company Holidays
- Flexible PTO policy
- Annual company-wide offsite
- Periodic team offsites
- Annual equipment stipend
- This role is open to candidates located anywhere in the United States.
Company Overview
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