Business Analytics Engineer, Customer Insights

Rad AI•,

1d•$150,000 - $190,000

About The Position

Rad AI is building embedded analytics into its reporting platform, giving radiology groups and health systems self-service visibility into reporting data such as turnaround times, template usage, AI utilization, and auto-launch accuracy. The Business Analytics Engineer will work across data engineering, analysis, and customer engagement to understand customer needs and build governed data models and dashboards that work across customers with varied data volumes, specialties, and reporting patterns. This is a customer-facing role involving radiology administrators, quality and operations leads, IT and informatics teams, and internal Product, Engineering, and Customer Success partners. The role translates ambiguous requests into rigorous, reusable metrics; connects a new data platform to the existing warehouse; establishes data contracts and data-quality practices; and builds semantic models and dashboards. It also requires statistical rigor in interpreting healthcare data, including attention to small sample sizes, outliers, seasonality, case mix, confidence intervals, risk adjustment, and caveats. The engineer will use AI agents to help describe, model, and document data relationships, while verifying their output, and will explain findings to clinical and executive audiences.

Requirements

2–5 years of experience in analytics engineering, data science, or business/BI analysis; customer- or stakeholder-facing experience is preferred within this requirement.
Demonstrated AI fluency, including recent concrete examples of using AI tools to describe, model, or document datasets and of checking the tools' output.
Strong SQL skills, including writing and optimizing complex queries.
Working statistical fluency, including confidence intervals, significance testing, sample-size considerations, handling outliers and skewed distributions, and judgment about when metrics need caveats.
Experience with a modern governed BI or semantic-layer tool and a SQL/Python notebook or exploration environment; examples include Omni or comparable tools such as Looker, Sigma, ThoughtSpot, Hex, or Mode.
Comfort working in dbt/Snowflake-style modeling environments and building reusable models.
Direct experience defining or maintaining a semantic layer, including entity or metric definitions, joins, or governed fields.
Comfort with ambiguity, undefined problems, and messy data.
Ability to conduct discovery with external stakeholders, ask follow-up questions, challenge vague requests, and arrive at defensible metric definitions.
Excellent written and verbal communication skills, including presenting data findings to nontechnical clinical audiences without oversimplifying.
Ability and willingness to verify AI-generated descriptions, models, and documentation.

Nice To Haves

Experience with healthcare, health IT, or data in another regulated industry, including HIPAA awareness.
Familiarity with radiology concepts such as PACS, HL7/FHIR, turnaround time, RVU, and template-driven reporting.
Experience building or maintaining customer-facing analytics products.
Exposure to multi-tenant data governance, such as row-level security and per-customer data isolation in embedded analytics.
Prior experience at a company selling to hospitals or health systems, including familiarity with procurement and IT review cycles.

Responsibilities

Connect the new data platform to the existing warehouse, troubleshoot customer-specific data-quality issues, and help define data contracts.
Gather and translate requirements through discovery with customers and internal partners, clarifying what stakeholders need to measure and decide.
Design and build the semantic layer, governed metrics, joins, reusable models, and dashboards for customer requirements.
Apply statistical rigor to healthcare reporting data, including appropriate treatment of small samples, outliers, seasonality, case mix, confidence intervals, risk adjustment, and exclusions.
Use AI agents to describe, model, and document data relationships, and verify their output.
Own data quality for customer-facing outputs, identify and explain anomalies, and document caveats such as sample-size exclusions, data lag, and definitional edge cases.
Build reusable data products using dbt/Snowflake-style modeling practices rather than creating one-off answers for individual customers.
Partner with Product, Engineering, and Customer Success to prioritize useful customer metrics and dashboards.
Present and explain findings, confidence intervals, and caveats to radiologists, hospital administrators, and executives.