Vice President, Data Warehouse Engineering

How you will help

The Vice President, Data Warehouse Engineering will drive the design, scalability, and optimization of HealthVerity's healthcare-focused data warehouse ecosystem—the most extensive and diverse in the United States. This role is ideal for a strategic, technical leader with deep expertise in healthcare data architecture, including the design of secure, scalable infrastructures capable of integrating and normalizing clinical, transactional, and operational data from thousands of healthcare providers and data partners. You will design and manage innovative, standards-compliant architectures that support complex, large-scale analytics and product development, shaping the future of healthcare data solutions.

What you will do

• Architect Scalable Data Solutions: Design and implement the enterprise-wide healthcare data warehouse architecture, integrating complex data sources such as EHR systems, claims data, lab results, and patient registries into a unified data platform.
• Healthcare-Specific Data Modeling: Build and optimize data models to support healthcare-specific use cases, including longitudinal patient analysis, population health, and clinical research. Leverage best practices in dimensional modeling, data vault modeling, and OLAP architectures.
• Standards-Based Integration: Ensure data interoperability by integrating data using standards like ANSI 837/835, HL7, FHIR, and ontologies like ICD-10, CPT, NDC, and LOINC. Architect solutions that facilitate smooth data exchange with external partners while ensuring data lineage and traceability.
• Data Governance and Quality: Establish robust data governance frameworks tailored to healthcare compliance requirements (HIPAA, HITECH). Implement tools and processes to monitor, cleanse, and validate incoming data streams, ensuring accuracy and consistency across all datasets.
• Data Pipeline Optimization: Architect high-performance ETL/ELT pipelines that efficiently ingest, transform, and load multi-terabyte datasets in near real time. Leverage Databricks DLT and workflows, Apache Airflow, dbt, or equivalent tools to automate data workflows while supporting both batch and streaming data ingestion.
• Cloud-Native Architecture: Design cloud-native solutions using AWS services like Redshift, S3, Glue, and Lambda and Databricks services like Unity catalog, Autoloader, and Delta Live Tables to manage high-throughput data ingestion and processing at scale. Explore hybrid models when on-premise integration is needed for sensitive healthcare data.
• Data Security & Privacy: Lead the implementation of security frameworks ensuring data confidentiality, integrity, and availability. Incorporate advanced security measures such as encryption (in transit and at rest), access controls, and secure data-sharing mechanisms.
• Master Data Management (MDM): Design and maintain master reference data systems, establishing consistency across providers, patients, medications, diagnoses, and procedures. Implement entity resolution algorithms to deduplicate and merge patient records.
• Advanced Query Performance: Optimize query execution for analytical workloads on large datasets using indexing, partitioning, and materialized views. Architect efficient reporting layers that provide sub-second query responses for mission-critical applications.
• Real-Time Data Integration: Design architectures to support real-time or near real-time data integration and streaming use cases using technologies like Apache Kafka, AWS Kinesis, or Google Pub/Sub.
• Cross-Functional Collaboration: Collaborate with data scientists, analysts, and product teams to design data marts and semantic layers that meet the needs of both operational and analytical workloads.
• Scalable Metadata Management: Implement metadata management and data cataloging tools (e.g., Apache Iceberg, Delta Tables, and Unity Catalog) to enhance data discovery, lineage tracking, and impact analysis.

Required skills and experience

• Extensive Data Architecture Expertise: 10+ years of hands-on experience designing, developing, and scaling large healthcare data warehouse ecosystems with multi-petabyte architectures.
• Healthcare Data Knowledge: Deep familiarity with healthcare-specific data sources (e.g., EHRs, claims, labs), and experience designing architectures compliant with healthcare regulations such as HIPAA and HITECH.
• Cloud-Native & Hybrid Solutions: Proven experience implementing cloud-based data platforms using AWS, Azure, or GCP. Experience with hybrid models that integrate on-premise data centers is a plus.
• Advanced Data Modeling: Strong knowledge of OLTP, OLAP, star schema, and snowflake schema designs, with an emphasis on healthcare data models. Experience with dimensional modeling and data