Data Architect/Modeling

We are seeking an experienced Data Modeler with a strong healthcare background to support Revenue Cycle Analytics initiatives. The candidate will collaborate with business stakeholders, analysts, and data engineering teams to gather reporting requirements, analyze source data, and design scalable data models that support accurate and consistent analytics. The ideal candidate will have strong hands-on experience in data modeling, SQL, and ERwin, along with a solid understanding of healthcare revenue cycle processes. Roles and Responsibilities

• Gather and document business requirements, reporting needs, revenue cycle metrics, and data definitions.
• Analyze source systems and datasets to identify data relationships, dependencies, and gaps.
• Develop and maintain conceptual, logical, physical, and dimensional data models using ERwin or similar tools.
• Define entities, attributes, keys, relationships, data granularity, historical tracking, and fact/dimension tables.
• Develop source-to-target mappings and document transformation logic, business rules, metadata, and data lineage.
• Use SQL for data profiling, reconciliation, relationship validation, and data quality assessment.
• Collaborate with data engineering teams to implement data models and integration workflows.
• Support implementation validation and User Acceptance Testing (UAT) to ensure datasets meet business and reporting requirements.
• Maintain data models, data dictionaries, mapping specifications, and technical documentation.

• Hands-on experience in data modeling for data warehouses, reporting, and analytics, including conceptual, logical, physical, and dimensional modeling.
• Experience with ERwin Data Modeler, ER/Studio, or similar enterprise data modeling tools.
• Healthcare domain experience, preferably in Revenue Cycle Analytics, including familiarity with healthcare reporting and revenue cycle metrics.
• Strong SQL skills for data profiling, reconciliation, validation, and data quality analysis.
• Experience with fact/dimension modeling, data relationships, keys, granularity, historical data management, and source-to-target mapping.
• Ability to translate business requirements into technical specifications and collaborate effectively with business stakeholders, analysts, and data engineering teams.
• Strong communication skills and the ability to work Consultantly and deliver within defined timelines.