Senior Healthcare Data Modeler

Abacus Insights

12d•Remote

About The Position

We are looking for a highly technical and hands-on Senior Healthcare Data Modeler to lead a global data modeling team responsible for transforming conceptual and logical data models into optimized physical ERDs. This role requires a deep understanding of healthcare data domains (Claims, Membership, Enrollment, Provider, Billing, Interoperability - FHIR, HL7, ADT, CCDA, etc.) and expertise in enterprise data modeling best practices. The Senior Healthcare Data Modeler will work closely with the Director of Data Modeling to define data modeling standards, metadata governance, and data architecture strategies that align with business and technical needs. They will also collaborate with engineering, client implementation teams, and data governance teams to ensure models support operational and analytical use cases, high performance, and data integrity. This is an exciting opportunity for a technical leader with expertise in Databricks, Snowflake, schema evolution, and modern data engineering practices to shape the future of healthcare data platforms.

Requirements

8+ years of experience in data modeling, enterprise data architecture, and cloud-based data engineering.
4+ years leading data modeling teams in enterprise environments.
Expertise in enterprise data modeling tools (Erwin, ER/Studio, DBSchema, or similar).
Strong hands-on experience with cloud data platforms (Databricks, Snowflake) and data lakehouse architectures.
Deep understanding of healthcare data models across Core Payer (Claims, Membership, Enrollment, Provider, Billing, Premium) and Clinical Interoperability (FHIR, HL7, ADT, CCDA, etc.) domains.
Experience with schema evolution, lossless data modeling, and data versioning strategies.
Strong background in SQL, performance tuning, and query optimization for large-scale datasets.
Hands-on experience with data engineering workflows, including data pipeline integration, ELT/ETL optimizations, and data transformation frameworks.
Ability to mentor junior modelers, enforce modeling best practices, and drive adoption of enterprise data standards.
Excellent collaboration skills to work with data architects, engineers, and client implementation teams.

Nice To Haves

Experience with OBT, data Lakehouse, and metadata management frameworks.
Familiarity with business intelligence (BI) and reporting solutions on modern cloud data warehouses.
Knowledge of APIs, SDKs, and data distribution frameworks for enabling real-time and batch data access.
Experience with data governance, security, and regulatory compliance in healthcare data management.

Responsibilities

Lead a global team of data modelers to build scalable, high-performance physical data models aligned with enterprise architecture and industry standards.
Define and enforce data modeling best practices, including schema evolution, lossless data modeling, metadata management, and data versioning.
Convert conceptual and logical data models into optimized physical ERDs using enterprise data modeling tools (Erwin, ER/Studio, DBT, or similar).
Ensure models support analytical (data science, actuarial, reporting) and operational (Claims, Care Management) use cases.
Maintain metadata, business glossary, and data dictionaries to support data lineage and governance.
Implement data quality rules and validation frameworks to meet industry SLAs and compliance requirements (HIPAA, HITRUST, CMS regulations).
Own data modeling workflows, including version control, schema deployment, and upgrade automation.
Work with engineering teams to implement data models in Databricks, Snowflake, and modern cloud architectures.
Optimize data models for query performance, indexing, partitioning, and storage efficiency.
Ensure data enrichment (standardization, transformations, algorithms, and grouping) is seamlessly integrated into the data models.
Design and document data delivery patterns using complex events or business rules to enable real-time and batch processing.
Implement data federation strategies, ensuring interoperability between enterprise data lakes, cloud data warehouses, and transactional systems.
Collaborate with Director of Data Modeling and client implementation teams to understand client needs, assess data model impact, and enhance models for efficient implementations.
Partner with engineering and product teams to define and prioritize data model features in the product roadmap.
Work with client management teams to provide support and technical expertise during client engagements.