Data Integration & Modelling Specialist (Clinical Data)

Overview

Remote
Depends on Experience
Contract - W2
Contract - Independent
Contract - 1 Year(s)
No Travel Required
Unable to Provide Sponsorship

Skills

Clinical Research
Data Integration

Job Details

Job Title: Data Integration & Modelling Specialist (Clinical Data)
Location: Remote
Duration: Long Term
Experience: 8 Years

 

Job Summary

The Data Integration & Modelling Specialist (Clinical) is responsible for integrating, harmonizing, and modelling complex clinical, imaging, and operational healthcare data to support analytics, machine learning, clinical decision support, and research initiatives. This role combines deep clinical domain knowledge with strong data engineering and data modelling expertise to ensure data is accurate, interoperable, analysis-ready, and clinically meaningful.

The specialist works across EHR systems, clinical registries, imaging platforms, and external data sources to build robust data pipelines and scalable data models that support downstream analytics, AI/ML development, regulatory reporting, and real-world evidence generation.

 

Key Responsibilities

Clinical Data Integration

  • Integrate structured and unstructured clinical data from multiple healthcare systems and sources, including EHRs, LIS, RIS, PACS, imaging archives, wearables, and external registries
  • Design and maintain ETL/ELT pipelines for ingesting clinical data at scale
  • Map and normalize data using healthcare interoperability standards (HL7, FHIR, DICOM, ICD-10, SNOMED CT, LOINC, CPT)
  • Resolve data quality issues related to missingness, inconsistency, duplication, and clinical ambiguity
  • Collaborate with clinical stakeholders to ensure integrated data accurately reflects clinical workflows and patient journeys

 

Data Modeling & Architecture

  • Design and implement logical and physical data models optimized for clinical analytics, reporting, and ML workloads
  • Develop patient-centric, longitudinal data models that unify encounters, diagnoses, procedures, medications, labs, and imaging
  • Support dimensional, relational, and feature-store data models depending on use case
  • Define data schemas, metadata, lineage, and versioning to ensure traceability and reproducibility
  • Optimize data models for performance, scalability, and regulatory compliance

 

Clinical Analytics & Modeling

  • Translate clinical questions into data requirements and analytical models
  • Support predictive modeling, cohort identification, outcome analysis, and real-world evidence generation
  • Engineer features from raw clinical data for statistical analysis and ML pipelines
  • Validate data transformations and models with clinicians and subject-matter experts
  • Ensure clinically appropriate interpretation of modeled outputs

 

Data Quality, Governance & Compliance

  • Implement data quality checks, validation rules, and monitoring processes
  • Support data governance initiatives including master data management and reference data alignment
  • Ensure compliance with healthcare regulations and privacy standards (HIPAA, GDPR, regional equivalents)
  • Contribute to audit-ready documentation for data sources, transformations, and models
  • Support de-identification, pseudonymization, and secure data access practices

 

Cross-Functional Collaboration

  • Work closely with clinicians, data scientists, ML engineers, product managers, and regulatory teams
  • Serve as a bridge between clinical users and technical teams, translating requirements in both directions
  • Support downstream teams with well-documented, analysis-ready datasets
  • Participate in design reviews, architecture discussions, and clinical validation sessions

 

Advanced / Optional Responsibilities

(Depending on seniority and organization)

  • Support integration of real-time or near-real-time clinical data streams
  • Contribute to ML feature stores and model monitoring pipelines
  • Assist with regulatory submissions or clinical study data preparation
  • Mentor junior data engineers or analysts

 

Required Qualifications

Education

  • Bachelor’s or Master’s degree in:
    • Health Informatics
    • Biomedical Engineering
    • Computer Science
    • Data Science
    • Clinical Sciences
    • Or a related field

 

Clinical Experience

  • Demonstrated experience working with clinical data in healthcare settings
  • Strong understanding of clinical workflows, terminology, and patient care processes
  • Experience collaborating directly with clinicians, researchers, or healthcare operations teams

 

Technical Skills

  • Strong SQL skills and experience with relational and analytical databases
  • Experience building data pipelines using Python, Spark, or similar tools
  • Hands-on experience with healthcare interoperability standards (FHIR, HL7, DICOM)
  • Experience with data modeling techniques (dimensional, normalized, patient-centric models)
  • Familiarity with cloud data platforms (AWS, Azure, Google Cloud Platform)
  • Experience with data orchestration and version control tools

 

Preferred Qualifications

  • Experience with EHR systems (Epic, Cerner, Meditech, or equivalents)
  • Experience integrating imaging and waveform data
  • Familiarity with clinical research, trials, or real-world evidence
  • Experience supporting AI/ML initiatives in healthcare
  • Knowledge of OMOP, i2b2, or other common clinical data models
  • Certification in health informatics or data architecture

 

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.