Senior Data Modeler

About this role

Our partner, a Fortune 500 company , is building scalable, analytics-ready data architectures for a client in the healthcare sector. We're looking for a Senior Data Modeller to design and maintain dimensional data models using Kimball methodology that power reporting and analytics at scale. You'll translate business requirements into well-structured logical and physical models, working closely with data engineers, BI teams, and business stakeholders — with particular emphasis on healthcare Encounters data.

This is a long-term, remote-first contract position with required US working hours overlap (2-6 PM CET). Responsibilities Dimensional modeling: design and maintain data models using Kimball methodology (star and snowflake schemas, fact and dimension tables, SCDs). Healthcare Encounters data: model Encounters data, ensuring accuracy and alignment with business and regulatory requirements. Advanced SQL: develop and optimize complex queries for data extraction, transformation, and validation.

Implementation: collaborate with data engineering teams to implement models within the data warehouse/lakehouse environment. Data quality: perform data profiling, quality checks, gap analysis, and troubleshooting across source and target systems. Documentation: maintain data models, data dictionaries, and mapping specifications. BI enablement: partner with BI and analytics teams to ensure models support downstream reporting needs.

Work Conditions Type: Full-time & Long-term contract work Start Date : ASAP Location : Remote US Time Zone Overlap : Required ( 2 PM - 6 PM CET , ideally until 7 PM CET) Contract with EU LLC Requirements Healthcare industry experience — including hands-on knowledge of Encounters data. Experience working with claims, member, or provider data in a healthcare payer (health insurer) or provider setting. Strong Kimball dimensional modeling background — star/snowflake schemas, fact and dimension tables, slowly changing dimensions (SCDs).

Advanced SQL skills — complex query writing, performance tuning, debugging, and data profiling across large datasets. Proven ability to design logical and physical data models for data warehouses/lakehouses optimized for reporting and analytics. Excellent problem-solving skills and keen attention to detail. Ability to collaborate with cross-functional teams and lead technical discussions with data engineers, BI teams, and business stakeholders.