We are seeking a highly specialized Senior Data Modeler to lead the architectural design and normalization strategy for our External Data Acquisition stream. This role is pivotal in transforming fragmented clinical data—specifically from CommonWell—into a high-performance, analytic-ready ecosystem. You will bridge the gap between complex interoperability standards (FHIR and CDA) and actionable business intelligence, ensuring our "Serve Layer" is both scalable and semantically accurate.
Core Responsibilities
As the lead architect for data acquisition, you will be responsible for the following key pillars:
1. FHIR Structure Design & Specification
Execute resource-level decomposition of incoming clinical bundles (Patient, Observation, Encounter, etc.) to ensure data integrity.
Translate technical HL7 FHIR specifications into business-aligned interpretations that stakeholders can leverage for clinical decision support.
2. Flattened FHIR Dataset Design
Architect a robust mapping strategy to transform nested FHIR JSON structures into flattened, analytic-friendly schemas (e.g., Parquet, SQL tables).
Develop reusable modeling patterns that allow for the rapid ingestion of new FHIR versions or additional external data sources.
3. Data Modeling Framework
Establish and maintain a Serve Layer alignment strategy, ensuring that acquired external data integrates seamlessly with internal clinical records.
Define and document standards for reuse across various clinical domains (Pharmacy, Lab, Claims) to minimize redundant engineering efforts.
4. CDA to FHIR Normalization Strategy
Provide deep-dive CDA/FHIR mapping insights, specifically targeting the nuances of CommonWell document exchanges.
Accelerate the curation and normalization of C-CDA documents into FHIR resources to ensure a longitudinal view of the patient record.
Qualifications & Requirements
FHIR Expertise: Proven experience designing and implementing FHIR-based data models (R4/R5). Deep understanding of Profiles, Extensions, and ValueSets.
CDA Domain Knowledge: Expert-level familiarity with Consolidated Clinical Document Architecture (C-CDA) and the challenges of parsing semi-structured clinical notes.
External Data Acquisition: Hands-on experience working with Health Information Exchanges (HIEs) or networks like CommonWell and Carequality.
Data Engineering Savvy: Proficiency in SQL and experience with modern data warehouse architectures (Snowflake, Databricks, or BigQuery).
Analytical Mindset: Ability to flatten deeply nested structures without losing clinical context or temporal accuracy.
Key Deliverables
FHIR Specification Documents: Detailed mapping of business entities to specific FHIR resources.
Mapping Logic: Validated transformation logic from nested JSON to relational/tabular formats.
Governance Standards: A documented framework for data reuse and semantic consistency across the enterprise.
Curation Playbook: A strategy for normalizing disparate CDA codes into standard terminologies (LOINC, SNOMED, RxNorm).