Senior Healthcare Data Analyst - Clinical Data Mapping & Quality

Qualified Health

1d•$120,000 - $160,000•Hybrid

About The Position

Qualified Health is seeking a Senior Healthcare Data Analyst to serve as the critical bridge between clinical concepts and technical data implementation. In this role, you'll combine deep healthcare domain expertise with analytical rigor to design and implement data mappings that ensure our AI platform accurately represents clinical reality. You'll work directly with health system partners to understand their data structures, develop comprehensive quality frameworks, and translate complex clinical workflows into standardized data models that power our platform's intelligence.

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

Nice To Haves

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)

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