Business Analytics Engineer, Customer Insights

Rad AI

. Connect the new data platform to the existing warehouse .

Posted 10/16/2026full-timeRemote • United StatesJuniorMid-Level💰 $150,000 - $190,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in analytics engineering and data science, with a strong focus on SQL, statistical analysis, and building governed semantic layers. Proven ability to communicate complex findings to non-technical stakeholders in the healthcare domain.

Highest-signal resume keywords

SQL Query OptimizationStatistical Analysis FluencyAI Fluency in Data ModelingExperience with BI ToolsCustomer-Facing Analytics Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLStatistical AnalysisData Quality ManagementData ModelingData VisualizationData Contract DefinitionAnomaly DetectionMetrics DefinitionData GovernanceSemantic Layer Maintenance

Soft Skills

Excellent CommunicationStakeholder EngagementCollaborationProblem-SolvingPresentation Skills

Tools & Technologies

DbtSnowflakeOmniLookerSigmaThoughtSpotHexMode

Industry Keywords

Healthcare DataHealth ITRadiology ConceptsPACSHL7FHIRMulti-Tenant Data GovernanceClinical Audiences

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Connect the new data platform to the existing warehouse
Troubleshoot and fix customer-specific data quality issues
Help define the data contract
Gather and translate requirements with radiology administrators, quality/operations leads, informatics teams, Product, and internal partners
Design and build governed semantic layers, metrics, joins, reusable models, and customer-facing dashboards
Apply statistical rigor to healthcare reporting data, including confidence intervals, risk adjustment, exclusions, and caveats
Direct AI agents to describe, model, and document data relationships and verify their outputs
Own data quality end-to-end for customer-facing outputs
Detect and explain anomalies and document sample-size exclusions, data lag, and definitional edge cases
Build durable, reusable data products using dbt/Snowflake-style modeling practices
Partner with Product, Engineering, and Customer Success to prioritize impactful metrics and dashboards
Present findings and explain metrics and statistical caveats to radiologists, hospital administrators, and executives

Requirements

What you’ll need

2–5 years in an analytics engineering, data science, or business/BI analyst role
Customer- or stakeholder-facing exposure
Demonstrated AI fluency, including recent examples of using AI to describe, model, or document datasets and verify outputs
Strong SQL, including writing and optimizing complex queries
Working statistics fluency: confidence intervals, significance testing, sample-size considerations, outlier and skewed-distribution handling
Experience with a modern governed BI/semantic layer tool such as Omni, Looker, Sigma, or ThoughtSpot
Experience with a SQL/Python notebook or exploration environment such as Hex or Mode
Comfort in dbt/Snowflake-style modeling environments
Direct experience defining or maintaining a semantic layer
Demonstrated ability to run discovery with external stakeholders and define defensible metrics
Excellent written and verbal communication for presenting findings to non-technical clinical audiences
Authorized to work lawfully in the US
Healthcare, health IT, or regulated-industry data experience is a strong plus
Familiarity with radiology concepts such as PACS, HL7/FHIR, turnaround time, RVU, and template-driven reporting is a bonus
Experience with customer-facing analytics products, multi-tenant data governance, or companies selling into hospitals/health systems is nice to have

Benefits

Comp & perks

Equity
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