Senior Healthcare Data Analyst - Clinical Data Mapping & Quality

Qualified Health is a company redefining possibilities in healthcare with Generative AI. They are seeking a Senior Healthcare Data Analyst to bridge clinical concepts and technical data implementation, ensuring accurate data mappings for their AI platform.

Responsibilities:

• Clinical Concept Mapping: Translate complex clinical concepts and workflows into explicit data element mappings across diverse source systems, ensuring semantic accuracy and clinical validity
• Query Development: Design and implement sophisticated SQL queries that conform source data to our Qualified Data Model while preserving clinical meaning and context
• Data Quality Leadership: Develop, maintain, and execute comprehensive QC checklists and validation frameworks to ensure data integrity across all clinical domains
• Client Partnership: Work directly with health system data teams, clinical informaticists, and IT departments to understand source schemas, reconcile mapping discrepancies, and optimize data extraction processes
• Schema Reconciliation: Lead technical discussions with customer teams to resolve data mapping conflicts, standardize terminology, and ensure consistent interpretation of clinical data elements
• Documentation & Standards: Create detailed mapping documentation, maintain data dictionaries, and establish standardized approaches for clinical data interpretation across EHR systems
• Cross-functional Collaboration: Partner with data engineers, clinical advisors, and product teams to ensure technical implementations align with clinical requirements and regulatory standards
• Continuous Improvement: Monitor data quality metrics, identify systematic issues, and recommend process improvements to enhance data accuracy and completeness
• Training & Knowledge Transfer: Develop training materials and conduct sessions for both internal teams and client organizations on proper data mapping and quality validation techniques

Requirements:

• 5+ years of experience in healthcare data analysis with demonstrated expertise in clinical data mapping and EHR data structures
• Deep clinical domain knowledge including understanding of clinical workflows, medical terminology, and healthcare delivery processes
• Advanced SQL proficiency with experience writing complex queries against healthcare databases and EHR systems
• EHR Systems Expertise: Hands-on experience with major EHR platforms (Epic, Cerner, AllScripts, or others) and understanding of their underlying data models
• Healthcare Data Standards: Working knowledge of FHIR, HL7, ICD-10, CPT, SNOMED, and other clinical coding systems
• Data Quality Experience: Proven track record developing and implementing data validation frameworks and quality assurance processes
• Client-facing Skills: Strong ability to work directly with health system stakeholders, translate technical concepts for clinical audiences, and manage complex data reconciliation projects
• Problem-solving Capabilities: Demonstrated ability to troubleshoot complex data mapping issues and develop creative solutions for non-standard data formats
• Communication Skills: Excellent written and verbal communication with ability to document complex mappings and present findings to varied technical and clinical audiences
• Bachelor's degree in Health Informatics, Healthcare Administration, Data Science, or related field
• Clinical Background: Clinical training, HIM certification, or formal healthcare informatics education
• FHIR Implementation Experience: Hands-on experience with FHIR mapping and transformation projects
• Epic Certification: Cogito, Caboodle, Clarity, or Chronicles training and certification
• Regulatory Knowledge: Understanding of HIPAA, HITECH, and healthcare data governance requirements
• Analytics Tools: Proficiency in Python, R, Tableau, or similar analytical platforms
• Project Management: Experience leading cross-functional data mapping initiatives
• Master's degree in relevant field
• Professional certifications in health informatics (RHIA, CHPS, or similar)