Healthcare Interoperability & Clinical Data Specialist

Job Description

Job Description

Healthcare Interoperability & Clinical Data SpecialistJob Summary

We are seeking an experienced Healthcare Interoperability & Clinical Data Specialist with deep hands-on expertise in CCD/C-CDA clinical documents, healthcare interoperability standards, and clinical data processing. This role will support a cloud-based healthcare data platform responsible for ingesting, parsing, validating, transforming, and normalizing clinical data from external healthcare partners.

The ideal candidate will have strong knowledge of CCD/C-CDA document structures, clinical sections, and terminology standards, combined with practical Python, SQL, and data integration experience. The current environment is primarily CCD/C-CDA-based, with a planned transition toward FHIR and API-driven interoperability. This is a hands-on technical role requiring candidates who can immediately contribute to clinical data extraction, mapping, and validation.

Key Responsibilities

• Analyze and interpret CCD/C-CDA documents, including clinical sections, templates, entries, identifiers, and coded observations, particularly Demographics, Vital Signs, Laboratory Results, Problems, Medications, and Procedures.

• Develop, enhance, and support Python-based solutions for parsing, extracting, transforming, and validating clinical data from CCD/C-CDA, XML, JSON, and other healthcare formats.

• Map clinical data elements into normalized data structures, including relational tables, dimensional models, and downstream reporting views.

• Support clinical data ingestion pipelines using AWS, PostgreSQL, Python, and SFTP-based integrations.

• Perform data profiling, reconciliation, and validation to identify missing clinical information, malformed documents, coding inconsistencies, and data quality issues.

• Develop and maintain data quality controls, error tracking, auditability, and processing metrics across large volumes of clinical data.

• Troubleshoot complex interoperability and data integration issues, perform root cause analysis, and implement technical solutions.

• Collaborate with EHR vendors, health information exchanges, data aggregators, and internal technology teams to onboard and validate new clinical data sources.

• Support the adoption of FHIR standards, including resource mapping, implementation guides, REST APIs, and future API-based data exchange.

• Support clinical data consumption for healthcare quality measurement, supplemental data reporting, care gap identification, and analytics.

Required Qualifications

• 5+ years of experience in healthcare data integration, clinical interoperability, healthcare data engineering, or a related technical discipline.

• Deep hands-on expertise with CCD/C-CDA documents, including the ability to identify, interpret, and extract clinical information from major document sections and templates.

• Demonstrated experience developing, modifying, or troubleshooting CCD/C-CDA parsing and clinical data extraction solutions.

• Strong Python development skills, including XML/JSON parsing, data transformation, automation, and validation.

• Strong SQL skills and experience working with relational databases, preferably PostgreSQL.

• Understanding of HL7 interoperability standards and clinical terminology, particularly LOINC and SNOMED CT.

• Working knowledge of FHIR resources, implementation guides, REST APIs, and clinical data mapping between CCD/C-CDA and FHIR.

• Experience with healthcare data ingestion, normalization, reconciliation, and data quality validation.

• Understanding of scalable data processing, error handling, logging, and operational monitoring.

• Strong analytical, troubleshooting, communication, and technical documentation skills.

Preferred Qualifications

• Experience with AWS cloud services, including Lambda and cloud-native data processing architectures.

• Experience with large-scale clinical data processing, distributed computing, or PySpark.

• Familiarity with dimensional data modeling, fact and dimension tables, and data processing layers such as Raw, Staging, Refined, and Published.

• Healthcare payer experience, particularly involving clinical data integration, supplemental data, or payer-provider interoperability.

• Knowledge of HEDIS, NCQA quality reporting, clinical quality measures, and care gap identification.

• Experience integrating clinical data from EHRs, health information exchanges, and clinical data aggregators.