Overview

We are seeking an experienced Senior Python Developer to support the Data Nexus Platform in building robust, regulatory-grade hospital data integration pipelines. The ideal candidate will have deep healthcare domain expertise, strong hands-on experience with Python and SQL, and expertise in transforming hospital EHR data into the OMOP Common Data Model (CDM). This role involves extracting consented patient data, mapping clinical terminologies, implementing high-standard data quality validation frameworks, and collaborating closely with health data engineers, QC teams, project leads, and client stakeholders.

Key Responsibilities

• Write Python/SQL code to transform hospital EHR data into OMOP CDM format (PERSON, VISITOCCURRENCE, CONDITIONOCCURRENCE, DRUGEXPOSURE, MEASUREMENT, etc.).
• Map clinical diagnosis codes (ICD-10), lab results (LOINC), and medications to OMOP standardized vocabularies (SNOMED-CT).
• Build hospital data integration pipelines to extract consented patient EHR data from European hospitals, pseudonymize, and standardize to OMOP CDM.
• Implement a 16-dimension data quality validation framework ensuring data completeness, accuracy, and regulatory compliance for FDA/EMA submissions.
• Develop Master Data Management (MPI) workflows for patient identity resolution, entity matching, deduplication, and golden record creation across multiple hospital systems.
• Build scalable ETL workflows for 8+ clinical domains including diagnoses, medications, lab results, procedures, clinical assessments, and biomarkers.
• Ensure regulatory-grade validation by proving lossless transformation, maintaining audit trails, and ensuring GxP compliance for pharmaceutical submissions.
• Debug complex data quality issues in real-world hospital datasets including missing values, coding inconsistencies, and schema variations.
• Document transformation logic, Source-to-Target Mapping (STTM) specifications, data lineage, and validation rules for audit compliance.
• Collaborate with hospital partners to understand clinical workflows and data structures.
• Support pharmaceutical clients in understanding data quality, OMOP standardization, and regulatory compliance.

Required Skills

• 5+ years working with hospital EMR/EHR systems (EPIC, Cerner, or similar) and real-world hospital data complexity.
• Hands-on OMOP CDM experience: Mapping hospital data to OMOP Common Data Model.
• Deep familiarity with clinical coding systems (ICD-10, SNOMED-CT, LOINC) and healthcare data standards (HL7, FHIR, EDI).
• Master Data Management (MPI): Patient identity resolution, entity matching, deduplication, and golden record creation.
• Advanced proficiency in Python + SQL for ETL development, data transformation, and validation logic.
• Data pipeline development: Building scalable, maintainable ETL/ELT workflows (pandas, PySpark, or similar).
• Data quality frameworks: Schema validation, referential integrity checks, reconciliation logic, null handling, and data profiling.

Preferred Skills

• Experience with cloud data infrastructure (AWS, GCP, Azure, or Oracle Cloud) and Git-based development workflows.
• GxP awareness and data governance (lineage tracking, metadata management, audit trail documentation).
• Academic health informatics or biomedical informatics background.
• Experience with oncology, immunology, clinical research datasets, or clinical trials data.
• Experience with data orchestration tools (Airflow, Azure Data Factory, Databricks, etc.).
• Knowledge of European hospital systems or GDPR compliance.

Collaboration & Communication

The resource will work as part of an established OMOP project team and will collaborate closely with:

• OMOP Developers
• QC and Data Validation teams
• Project/Delivery leads
• Client stakeholders

The role requires participation in regular team discussions, project meetings, and client-facing calls.

Tools & Technologies

• Data Model: OMOP Common Data Model (CDM)
• Data Engineering: Python, SQL, PySpark, pandas, Databricks, Airflow / Azure Data Factory
• Healthcare Data: Hospital EHR/EMR (EPIC, Cerner), Clinical domain datasets (Diagnoses, Labs, Medications, Biomarkers), Oncology/Immunology
• Data Management & Governance: Source-to-Target Mapping (STTM), Master Data Management (MPI / Golden Record), 16-Dimension Data Quality Frameworks, Audit Trails & GxP Compliance
• Healthcare Standards & Vocabularies: ICD-10, SNOMED-CT, LOINC, HL7, FHIR, EDI