Oncology Health Partners
Oncology Health Partners
(OHP or Company) is a next-generation management services organization dedicated to enabling world-class cancer care services within health systems. OHP does so through a partnership model that expands and enhances the oncology service lines of its health system partners through dedicated investment, a technology and analytical backbone, and a unique ecosystem of health system operators and physicians. The Company was founded by a group of executives and investors with unique expertise in oncology care and health system partnerships, having previously founded American Oncology Resources, The US Oncology Network, OneOncology, and Select Medical, with additional executive experiences at Flatiron Health and Evidation Health. Summary
OHP is seeking to expand the Product Engineering team by adding a Data Architecture Lead. This person will work closely with Ed Rodgers (SVP, Product Innovation and Analytics) and be an integral part of the Companys expansion of technical capabilities, including design and build of an enterprise transactional database, data warehouse, and analytics infrastructure strategy that will power the Companys innovative product initiatives. This is a critical leadership role that will shape how we collect, store, transform, and utilize healthcare data to improve clinical workflow, optimize clinical performance, uncover intelligent insights, and improve patient outcomes. This person will have Day 0 input into the selection of technical stack and implementation strategies. This person will be responsible for architecting scalable data solutions, optimizing database performance, building robust ETL/ELT pipelines, and ensuring the Companys data infrastructure can support sophisticated AI/ML workloads while maintaining the highest standards of security and HIPAA compliance. This dynamic role requires a multi-dimensional skillset, including the ability to operate independently, liaise with health system operators and executives, build and manage a team, contribute to key deal activities, and communicate effectively. This person should be an experienced and highly motivated self-starter, comfortable voicing their opinion and asking questions, and able to make decisions in ambiguous situations. Responsibilities Data Architecture & Strategy Design and implement comprehensive data warehouse architecture using modern cloud-native patterns Develop data modeling strategies for clinical, operational, and financial data across multiple healthcare systems Architect data lakes and data warehouses optimized for both operational analytics and AI/ML workloads Define data governance frameworks, data quality standards, and metadata management practices Create scalable data architecture roadmaps that support business growth and AI initiatives Design, implement, and optimize PostgreSQL database clusters for high-performance analytical workloads Build and maintain specialized databases for AI/ML feature stores and model training datasets Implement database partitioning, indexing, and query optimization strategies for large-scale data Design and manage time-series databases for temporal monitoring and trend analysis Contribute perspective to the cloud architect related to database security, backup, and disaster recovery strategies for mission-critical healthcare data Performance tune databases supporting both OLTP and OLAP workloads Architect and build robust, scalable ETL/ELT pipelines for ingesting healthcare data from multiple sources Develop real-time and batch data processing workflows using Python, SQL, and cloud-native tools (or comparable alternatives) Implement data transformation processes for HL7, FHIR, and other healthcare data standards Build automated data quality monitoring, validation, and error handling mechanisms Design and maintain data integration patterns for clinical systems, EHRs, and external healthcare APIs Optimize data pipeline performance for high-volume, low-latency requirements
AI/ML Data Infrastructure (Year 2+)
Design feature engineering pipelines, feature stores, data versioning, lineage tracking, and reproducibility frameworks for machine learning models Build data preprocessing and transformation workflows specifically for AI/ML training and inference Architect real-time data streaming infrastructure for AI model serving and monitoring Design A/B testing data infrastructure for AI model performance evaluation Build scalable data infrastructure to support model retraining and continuous learning workflows
Healthcare Data Compliance & Security
Ensure all data infrastructure meets HIPAA, HITECH, and other healthcare regulatory requirements with a goal of achieving HITRUST certification or ISO27001 certification within 18 months. Implement de-identification and anonymization processes for patient data used in AI/ML (with organization support for certifying these models by an external certifying body) Design audit trails and access controls for all protected health information (PHI) Build data encryption, tokenization, and secure data sharing mechanisms Work with security teams to implement zero-trust data access patterns
Clinical Data Integration
Provide perspective to the integration engineering team so that integrations with Electronic Health Records (EHR) systems and clinical data repositories are of high fidelity Implement HL7 FHIR data transformation and standardization processes Design clinical data warehouses that support both operational reporting and can scale to support for predictive analytics Build patient longitudinal record aggregation and clinical timeline construction Provide requirements input to the clinical decision support engineering team for clinical decision support data models and real-time analytics capabilities
Required Qualifications
7+ years of experience in data architecture, database engineering, or related data infrastructure roles 5+ years of comprehensive, hands-on experience with PostgreSQL (or similar platform experience) 3+ years of experience building data warehouses and ETL/ELT pipelines at scale 2+ years of experience with cloud data platforms (preferably Azure Data Factory, Synapse, Data Lake) Experience with Python (or comparable common language) for data engineering, transformation, and pipeline development Knowledge of healthcare data standard modeling (HL7, FHIR, DICOM) and clinical data structures Experience building data infrastructure for AI/ML workloads and model training pipelines Understanding of HIPAA compliance and healthcare data security requirements Strong knowledge of data pipeline orchestration tools (Apache Airflow, Azure Data Factory, or similar) Experience with real-time data streaming technologies (Apache Kafka, Azure Event Hubs, or similar)
Preferred Qualifications
Master's degree in Computer Science, Data Engineering, or related quantitative field Experience with Azure cloud data services (Synapse Analytics, Data Lake Storage, Cosmos DB) Knowledge of machine learning operations (MLOps) and model deployment pipelines Experience with Apache Spark, Databricks, or other big data processing frameworks Familiarity with NoSQL databases (MongoDB, Cassandra) and time-series databases (InfluxDB, TimescaleDB) Experience with data cataloging tools (Apache Atlas, Azure Purview) and metadata management Experience with containerization (Docker, Kubernetes) for data pipeline deployment Certifications in cloud data platforms (Azure Data Engineer, AWS Data Analytics, etc.) Experience with graph databases for healthcare relationship modeling Knowledge of statistical analysis and data science methodologies
Technical Environment
This person will architect and work with our comprehensive data stack: Seniority level: Director Employment type: Full-time Job function: Engineering and Information Technology Industries: Hospitals and Health Care
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(OHP or Company) is a next-generation management services organization dedicated to enabling world-class cancer care services within health systems. OHP does so through a partnership model that expands and enhances the oncology service lines of its health system partners through dedicated investment, a technology and analytical backbone, and a unique ecosystem of health system operators and physicians. The Company was founded by a group of executives and investors with unique expertise in oncology care and health system partnerships, having previously founded American Oncology Resources, The US Oncology Network, OneOncology, and Select Medical, with additional executive experiences at Flatiron Health and Evidation Health. Summary
OHP is seeking to expand the Product Engineering team by adding a Data Architecture Lead. This person will work closely with Ed Rodgers (SVP, Product Innovation and Analytics) and be an integral part of the Companys expansion of technical capabilities, including design and build of an enterprise transactional database, data warehouse, and analytics infrastructure strategy that will power the Companys innovative product initiatives. This is a critical leadership role that will shape how we collect, store, transform, and utilize healthcare data to improve clinical workflow, optimize clinical performance, uncover intelligent insights, and improve patient outcomes. This person will have Day 0 input into the selection of technical stack and implementation strategies. This person will be responsible for architecting scalable data solutions, optimizing database performance, building robust ETL/ELT pipelines, and ensuring the Companys data infrastructure can support sophisticated AI/ML workloads while maintaining the highest standards of security and HIPAA compliance. This dynamic role requires a multi-dimensional skillset, including the ability to operate independently, liaise with health system operators and executives, build and manage a team, contribute to key deal activities, and communicate effectively. This person should be an experienced and highly motivated self-starter, comfortable voicing their opinion and asking questions, and able to make decisions in ambiguous situations. Responsibilities Data Architecture & Strategy Design and implement comprehensive data warehouse architecture using modern cloud-native patterns Develop data modeling strategies for clinical, operational, and financial data across multiple healthcare systems Architect data lakes and data warehouses optimized for both operational analytics and AI/ML workloads Define data governance frameworks, data quality standards, and metadata management practices Create scalable data architecture roadmaps that support business growth and AI initiatives Design, implement, and optimize PostgreSQL database clusters for high-performance analytical workloads Build and maintain specialized databases for AI/ML feature stores and model training datasets Implement database partitioning, indexing, and query optimization strategies for large-scale data Design and manage time-series databases for temporal monitoring and trend analysis Contribute perspective to the cloud architect related to database security, backup, and disaster recovery strategies for mission-critical healthcare data Performance tune databases supporting both OLTP and OLAP workloads Architect and build robust, scalable ETL/ELT pipelines for ingesting healthcare data from multiple sources Develop real-time and batch data processing workflows using Python, SQL, and cloud-native tools (or comparable alternatives) Implement data transformation processes for HL7, FHIR, and other healthcare data standards Build automated data quality monitoring, validation, and error handling mechanisms Design and maintain data integration patterns for clinical systems, EHRs, and external healthcare APIs Optimize data pipeline performance for high-volume, low-latency requirements
AI/ML Data Infrastructure (Year 2+)
Design feature engineering pipelines, feature stores, data versioning, lineage tracking, and reproducibility frameworks for machine learning models Build data preprocessing and transformation workflows specifically for AI/ML training and inference Architect real-time data streaming infrastructure for AI model serving and monitoring Design A/B testing data infrastructure for AI model performance evaluation Build scalable data infrastructure to support model retraining and continuous learning workflows
Healthcare Data Compliance & Security
Ensure all data infrastructure meets HIPAA, HITECH, and other healthcare regulatory requirements with a goal of achieving HITRUST certification or ISO27001 certification within 18 months. Implement de-identification and anonymization processes for patient data used in AI/ML (with organization support for certifying these models by an external certifying body) Design audit trails and access controls for all protected health information (PHI) Build data encryption, tokenization, and secure data sharing mechanisms Work with security teams to implement zero-trust data access patterns
Clinical Data Integration
Provide perspective to the integration engineering team so that integrations with Electronic Health Records (EHR) systems and clinical data repositories are of high fidelity Implement HL7 FHIR data transformation and standardization processes Design clinical data warehouses that support both operational reporting and can scale to support for predictive analytics Build patient longitudinal record aggregation and clinical timeline construction Provide requirements input to the clinical decision support engineering team for clinical decision support data models and real-time analytics capabilities
Required Qualifications
7+ years of experience in data architecture, database engineering, or related data infrastructure roles 5+ years of comprehensive, hands-on experience with PostgreSQL (or similar platform experience) 3+ years of experience building data warehouses and ETL/ELT pipelines at scale 2+ years of experience with cloud data platforms (preferably Azure Data Factory, Synapse, Data Lake) Experience with Python (or comparable common language) for data engineering, transformation, and pipeline development Knowledge of healthcare data standard modeling (HL7, FHIR, DICOM) and clinical data structures Experience building data infrastructure for AI/ML workloads and model training pipelines Understanding of HIPAA compliance and healthcare data security requirements Strong knowledge of data pipeline orchestration tools (Apache Airflow, Azure Data Factory, or similar) Experience with real-time data streaming technologies (Apache Kafka, Azure Event Hubs, or similar)
Preferred Qualifications
Master's degree in Computer Science, Data Engineering, or related quantitative field Experience with Azure cloud data services (Synapse Analytics, Data Lake Storage, Cosmos DB) Knowledge of machine learning operations (MLOps) and model deployment pipelines Experience with Apache Spark, Databricks, or other big data processing frameworks Familiarity with NoSQL databases (MongoDB, Cassandra) and time-series databases (InfluxDB, TimescaleDB) Experience with data cataloging tools (Apache Atlas, Azure Purview) and metadata management Experience with containerization (Docker, Kubernetes) for data pipeline deployment Certifications in cloud data platforms (Azure Data Engineer, AWS Data Analytics, etc.) Experience with graph databases for healthcare relationship modeling Knowledge of statistical analysis and data science methodologies
Technical Environment
This person will architect and work with our comprehensive data stack: Seniority level: Director Employment type: Full-time Job function: Engineering and Information Technology Industries: Hospitals and Health Care
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