The posting
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Healthcare Interoperability & Clinical Data Specialist based in the United States.
This role focuses on onboarding, transforming, validating, and supporting clinical data from diverse healthcare sources and interoperability partners. You will work with healthcare data standards including CCD/C-CDA, HL7, FHIR, and REST APIs to make complex clinical information usable and reliable. The position combines healthcare interoperability, data engineering, clinical data quality, analytics, and technical troubleshooting. You will help integrate data from EHR vendors, health information exchanges, aggregators, and internal systems into scalable and normalized data models. A key part of the role is assessing data quality, clinical usability, terminology, and suitability for quality measurement and analytics use cases. You will collaborate across technical, clinical, and business teams to resolve integration challenges and establish reliable data pipelines. This opportunity is well suited to an experienced healthcare data professional who enjoys working at the intersection of clinical information, interoperability standards, and data technology.
Accountabilities:
- Analyze and interpret clinical data from new and existing sources, with a focus on CCD/C-CDA documents, FHIR resources, HL7 messages, and API-based exchanges.
- Evaluate the structure, semantics, completeness, consistency, and clinical usability of data from EHRs, health information exchanges, aggregators, and other interoperability partners.
- Develop, enhance, and maintain processes for parsing, extracting, normalizing, transforming, and validating XML, JSON, delimited files, APIs, and other healthcare data formats.
- Map source data elements and clinical concepts to normalized internal models and, where applicable, FHIR resources and standard terminologies.
- Lead or support onboarding of new clinical data sources through source discovery, sample-file analysis, field and code mapping, profiling, validation, defect identification, and production-readiness assessment.
- Create automated data quality checks and reconciliation processes to identify missing sections, malformed documents, inconsistent coding, duplicate clinical facts, unexpected values, and source-specific anomalies.
- Investigate interoperability and integration issues, conduct root-cause analysis, and coordinate resolution with vendors, exchanges, aggregators, IT teams, and business stakeholders.
- Develop and maintain reusable Python and SQL utilities for data profiling, parsing, comparison, validation, exception handling, and large-scale analysis.
- Work with FHIR resources and RESTful APIs to retrieve, inspect, validate, and transform clinical data, including resource relationships, references, profiles, bundles, and terminology bindings.
- Analyze CCD/C-CDA documents at both document and section level, including templates, entries, identifiers, codes, dates, providers, encounters, observations, and procedures.
- Assess clinical data sources for downstream use cases such as quality measurement, supplemental data, clinical analytics, population health, and HEDIS reporting.
- Create and maintain source inventories, interface specifications, data dictionaries, mapping documents, validation rules, data lineage, known limitations, and onboarding procedures.
- Participate in user acceptance testing, production support, source certification, release validation, and ongoing monitoring of interoperability and clinical data pipelines.
- Bachelor’s degree in Computer Science, Information Systems, Health Informatics, Data Analytics, Health Information Management, or a related field, or equivalent practical experience.
- 5+ years of experience working with healthcare data, healthcare interoperability, clinical data integration, health informatics, or a closely related field.
- Hands-on experience analyzing and working with CCD/C-CDA clinical documents, including XML structures, sections, entries, templates, identifiers, and clinical coding.
- Hands-on experience with FHIR, including common clinical resources, Bundles, references, profiles, REST APIs, and JSON/XML representations.
- Strong SQL skills and practical Python experience for parsing, transformation, automation, validation, and analysis of large healthcare datasets.
- Working knowledge of healthcare interoperability standards and concepts, including HL7, C-CDA/CCD, FHIR, APIs, and common healthcare code systems and terminologies.
- Experience profiling unfamiliar healthcare data sources and translating source-specific structures into standardized or normalized data models.
- Experience troubleshooting data quality, mapping, ingestion, and integration issues across multiple vendors or source systems.
- Strong analytical and problem-solving abilities, with excellent communication and technical documentation skills.
- Ability to explain complex clinical data and integration issues clearly to both technical and business stakeholders.
- Experience with HEDIS, NCQA quality reporting, supplemental data, digital quality measurement, or Medical Record Review is preferred.
- Experience evaluating clinical data for quality-measure use cases and identifying document sections, coded facts, and source types that provide meaningful reporting value is preferred.
- Knowledge of clinical terminologies and value-set concepts, including LOINC, SNOMED CT, ICD-10-CM, CPT/HCPCS, and RxNorm is preferred.
- Experience with AWS or other cloud-based data and analytics environments, including large-scale file processing and automated data pipelines, is a plus.
- Experience working with health information exchanges, EHR vendors, clinical data aggregators, or payer-provider interoperability initiatives is preferred.
- Remote contract opportunity available to candidates across the United States.
- Opportunity to work on healthcare interoperability and clinical data initiatives with broad downstream applications.
- Exposure to modern healthcare data standards and technologies, including FHIR, C-CDA, HL7, REST APIs, Python, SQL, and cloud-based data environments.
- Opportunity to contribute to clinical analytics, population health, quality measurement, supplemental data, and HEDIS-related use cases.
- Collaborative environment involving technical teams, clinical quality stakeholders, EHR vendors, health information exchanges, and data partners.
- Opportunity to develop scalable data onboarding, normalization, validation, and monitoring processes.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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