The Senior Data Architect is responsible for designing, implementing, and managing the data architecture that supports Valley Health System (VHS) analytics and business intelligence initiatives within the healthcare sector. This role requires a deep understanding of data modeling, data integration, and data warehousing, as well as the ability to work collaboratively with cross-functional teams to deliver high-quality data solutions. The Senior Data Architect is responsible for designing and implementing enterprise data architecture that supports advanced analytics, clinical decision-making, population health, finance, revenue cycle, planning, and regulatory compliance. This role ensures that data systems are scalable, secure, and aligned with healthcare-specific goals, enabling data-driven insights across clinical, operational, and financial domains. 

Responsibilities and Duties

Architecture Design: Develops and maintains robust data architectures (cloud and on-prem) that support analytics, reporting, and operational workflows and integrate EHR/EMR systems (e.g., Epic, Cerner), claims data, HL7/FHIR interfaces, and patient-reported outcomes.

Data Modeling: Creates and manages conceptual, logical, and physical data models that reflect business processes and analytical needs. 

ETL & Data Integration: Designs and oversees ETL pipelines and data integration strategies to ensure efficient, accurate, and timely data flow from diverse sources. 

Data Governance & Quality: Defines and enforces data standards, metadata management, and data quality frameworks to ensure trust in enterprise data assets.  Implements frameworks to ensure HIPAA compliance, PHI protection, and adherence to ONC/CMS interoperability rules. 

Collaboration: Partners with data engineers, BI developers, analysts, and business stakeholders to translate business requirements into scalable data solutions. 

Security & Compliance: Implements data security protocols, access controls, and compliance measures (e.g., HIPAA, GDPR) to protect sensitive information. 

Terminology & Standards: Normalizes data using SNOMED CT, LOINC, ICD-10, CPT, and RxNorm to support semantic interoperability. 

Performance Optimization: Continuously monitors and enhances data system performance, including indexing, partitioning, and query tuning. 

Documentation & Communication: Maintains comprehensive documentation of data architecture, lineage, and best practices; communicate technical concepts to non-technical audiences. 

Education

Bachelor's degree in a quantitative-oriented field such as Business, Economics, Statistics, Biostatistics, Epidemiology, Information Technology, or Healthcare Administration or commensurate years of experience, is required.

Experience

7+ years of relevant experience in design and implementation of data models for enterprise data warehouse initiatives is required.

Experience leading projects involving data warehousing, data modeling, and data analysis is required. 

Experience with healthcare interoperability standards (FHIR, HL7, CCD) is required. 

Experience with Epic Caboodle, Clarity, or other clinical data warehouses is required. 

Experience with modern data platforms (e.g., Snowflake, Databricks), ETL tools (e.g., SSIS, Informatica), and BI tools (e.g., Power BI, Tableau) is required. 

Qualification/Skills 

Proficiency in SQL, Python, and cloud platforms (Azure, AWS, GCP). 

Proficiency in data modeling tools, SQL, and cloud platforms (e.g., Azure, AWS). 

Strong understanding of HIPAA, HITECH, and healthcare data privacy regulations. 

Strong understanding of data governance, security, and compliance frameworks. 

Excellent communication and stakeholder engagement skills.