Publié avant-hier
Data Engineer - Clinical Data Experience
@ Kaizen Technologies
Toronto, Ontario
En bref
Kaizen Technologies is hiring a contract Python and PySpark developer in Toronto to build clinical document pipelines. The work centres on parsing CCD/C-CDA clinical XML into structured data across multiple EHR vendors.
Responsabilités :
- Build Python CCD/C-CDA parsing and clinical processing solutions
- Extract demographics, medications, labs and other clinical data from XML
- Develop scalable Python and PySpark data pipelines
- Onboard new vendors into a reusable framework
- Troubleshoot data-quality issues and production incidents
Résumé des exigences :
- Strong hands-on Python and PySpark experience
- Practical CCD/C-CDA XML parsing experience
- Strong SQL and relational database experience
- Knowledge of clinical terminologies such as LOINC, SNOMED CT and ICD-10
- Experience with multiple EHR vendor implementations
Outils et compétences
Source et dates
Type de source non vérifié · apify-linkedin
- Date publiée par l’employeur
- 2026-11-13
- Première détection par notre source
- 2026-11-15
- Mise à jour du dossier source
- 2026-11-15
Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.
Détails du poste
| Niveau | Senior |
| Domaine | Développement logiciel |
| Expérience | 15 an(s) et plus |
| Déplacements | avion : aucun · route : aucun |
| Milieu de travail | Bureau |
| Position | Surtout assis |
| Exigences physiques | physique faible · ordinateur élevé |
| Langues | English |
Résumé et détails rédigés par l’IA à partir de l’offre; l’offre de l’employeur fait foi.
Kaizen Technologies
| Champ | Valeur |
|---|---|
| Offres sur Jobsy | 1 |
Job Title : Data Engineer - Clinical Data Experience
Location: TORONTO, Canada
Skill Cluster : DE-Python_Full Stack (with SQL), Frameworks, DevOps.
Primary Skill : Python
Experience : 15 years
Job Description :
- We are looking for a hands-on Python / PySpark Developer with practical experience processing CCD/C-CDA clinical documents and building scalable healthcare data pipelines
Key Responsibilities
- Develop and enhance Python-based CCD/C-CDA parsing and clinical data processing solutions.
- Analyze raw CCD/C-CDA XML and extract clinical information such as demographics, encounters, diagnoses, medications, allergies, immunizations, vitals, labs, and procedures.
- Build reusable and scalable parsing logic that can support multiple EHRs, HIEs, vendors, and CCD variations.
- Onboard new CCD vendors into an existing framework with minimal vendor-specific code.
- Develop scalable data processing pipelines using Python and PySpark.
- Handle large numbers of files and large XML documents efficiently.
- Implement validation, error handling, logging, reconciliation, retry, and source-lineage capabilities.
- Troubleshoot missing, duplicate, malformed, or incorrectly interpreted clinical data.
- Optimize processing performance and support production incidents and root-cause analysis.
- Develop automated unit and regression tests to ensure new vendor changes do not impact existing integrations.
- Work with upstream providers and internal teams to resolve data-quality and clinical mapping issues.
Required Skills
- Strong hands-on Python development.
- Strong PySpark and large-scale data processing experience.
- Practical experience with XML parsing and CCD/C-CDA documents.
- Experience developing or significantly enhancing CCD/C-CDA parsers.
- Good understanding of clinical data domains such as Labs, Vitals, Problems, Medications, Immunizations, Allergies, Procedures, and Encounters.
- Experience handling different vendor/EHR implementations of CCD/C-CDA.
- Strong SQL and relational database experience.
- Experience with clinical terminology such as LOINC, SNOMED CT, ICD-10, CPT/HCPCS, RxNorm, and CVX.
- Experience designing modular, reusable, and configuration-driven solutions rather than vendor-specific hardcoded parsers.
Preferred Skills
- AWS experience including S3, Lambda, Glue, ECS/Fargate, Step Functions, SQS/SNS, CloudWatch, or similar services.
- Experience with Epic, Cerner, Meditech, HIEs, or other EHR/clinical data sources.
- Healthcare payer, population health, or clinical quality reporting experience.
- HEDIS / NCQA knowledge is an advantage but not mandatory
Journal
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