Data Modeler

UDMH Data Modeler Analytics & Insights Managed Services | Healthcare Industry | Senior Associate Position UDMH Data Modeler — Senior Associate Level Senior Associate Engagement Type Managed Services Environment IBM UDMH | DB2 / IBM IIAS | Operational Data Store | Enterprise Data Warehouse | Clinical Data Integration | IBM DataStage | Oracle GoldenGate | AWS Tools (Must) IBM Unified Data Model for Healthcare (UDMH) | DB2 | IBM DataStage | ER/Studio or ERwin Tools (Good to have) Collibra | IBM InfoSphere | SQL Server | AWS Redshift Certifications Preferred IBM Data Management or equivalent Data Modelling Certification Industry Healthcare (US) — Claims, Membership, Encounters, Clinical, Pharmacy, Population Health Position Summary The UDMH Data Modeler is a core technical contributor within the Healthcare Analytics & Insights Managed Services engagement, responsible for the design, development, and governance of enterprise data models across the Operational Data Store (ODS), Enterprise Data Warehouse (EDW), and Clinical Data Integration (CDI) platforms. This role leverages the IBM Unified Data Model for Healthcare (UDMH) framework to build and maintain the canonical, multi-tenant, multi-source data models that underpin enterprise reporting, operational analytics, and clinical decision-making across health plan, delivery system, and population health domains. The UDMH Data Modeler works closely with data engineers, BI developers, and business stakeholders to ensure data structures are accurate, scalable, and aligned to evolving business requirements. Key Responsibilities Design, develop, and maintain enterprise logical and physical data models using the IBM Unified Data Model for Healthcare (UDMH) framework across Operational Data Store (ODS), Enterprise Data Warehouse (EDW), and Clinical Data Integration environmentsBuild and govern the canonical multi-tenant, multi-source data model in IBM IIAS (DB2) — including atomic normalised structures, hub-spoke relationships, and subject area designs for claims, membership, encounters, pharmacy, and clinical domainsDevelop and maintain source system logical data models for operational data stores and clinical source integrations — mapping source structures to the canonical UDMH modelDefine and enforce data modelling standards, naming conventions, entity-relationship (ER) design patterns, and data vault / hybrid modelling practices across the engineering teamCollaborate with data engineers to ensure ETL jobs and CDC pipelines correctly populate and maintain model structures with appropriate SCD Type 1 and Type 2 handlingLead impact analysis for model changes — assessing downstream effects on ETL jobs, scheduled job streams, data mart layers, BI dashboards, and analytics outputsSupport the EDW medallion lakehouse architecture (Bronze / Silver / Gold layers) — designing Raw Data Vault, Business Data Vault, and Simplified Data Access Layer structures on analytical database platformsParticipate in data governance initiatives — maintaining data dictionaries, lineage documentation, and metadata in enterprise governance tools aligned to HIPAA and HITRUST compliance requirementsWork with business analysts and clinical informatics teams to translate business requirements into scalable data model designs across health plan, delivery system, and population health domainsConduct peer reviews of data model designs and ETL-to-model mapping specifications, ensuring quality and adherence to UDMH standardsSupport AWS data layer modelling for analytics migration — designing Redshift schemas, S3-based data lake structures, and Glue Data Catalog definitions aligned to the modernization roadmapIdentify and drive data model rationalization — consolidating redundant structures, retiring obsolete entities, and streamlining the canonical model to reduce complexity and improve query performanceOwn L2/L3 escalations for data model-related incidents — schema mismatches, referential integrity failures, SCD logic errors, and canonical model breaksMaintain comprehensive documentation including data model diagrams, ER diagrams, data dictionaries, mapping specifications, and onboarding guides for the data engineering teamLead knowledge management and continuity documentation for all supported data model components Required Qualifications Minimum Degree Required: Bachelor’s Degree in Computer Science, Information Systems, Engineering, Statistics, Mathematics, or a related quantitative field5–8 years of enterprise data modelling experience with deep expertise in logical and physical data model design, ER modelling, and large-scale data warehouse architectureHands-on experience with IBM Unified Data Model for Healthcare (UDMH) — including canonical model design, multi-tenant architecture, and hub-spoke entity structuresExpert-level proficiency in IBM DB2 — schema design, complex query writing, stored procedures, index management, and performance tuning on IBM IIAS applianceStrong experience with data vault modelling concepts — Hubs, Links, Satellites, and Business Data Vault patternsProficiency in data modelling tools such as ER/Studio, ERwin, or IBM InfoSphere Data ArchitectSolid understanding of SCD Type 1 and Type 2 implementation patterns and their impact on downstream ETL and reporting layersHands-on IBM DataStage experience — understanding of how ETL job design maps to physical data model structures, partitioning strategies, and load patternsExperience working with Netezza Performance Server (NPS) or equivalent MPP analytical databases — distribution key design, zone maps, and query optimizationStrong understanding of star schema, snowflake schema, and dimensional modelling concepts for enterprise data warehouse environmentsExperience with data governance tools and practices — data lineage, metadata management, data dictionary maintenance, and HIPAA-compliant data access controlsProven ability to lead data model impact analysis, manage cross-team dependencies, and communicate model changes to technical and non-technical stakeholdersExperience operating within ITSM frameworks (ServiceNow or equivalent) for intake, change management, and delivery tracking Preferred Qualifications US Healthcare industry experience across claims, clinical (HL7/FHIR), EMR, pharmacy, HEDIS, population health, or health plan domainsExperience with clinical data integration canonical model design — integrating HL7 ORU, ADT, and CCDA clinical data structuresFamiliarity with Collibra or IBM InfoSphere for enterprise data governance, lineage tracking, and business glossary managementAWS data modelling experience — Redshift schema design, S3-based data lake structures, Glue Data Catalog, and lake house architecture patternsExposure to Epic Clarity / Caboodle data models — understanding of how clinical EHR data surfaces in ODS and EDW environmentsKnowledge of HIPAA, HITRUST, CMS reporting requirements, and healthcare regulatory data standards relevant to PHI data modellingExperience supporting on-premises to cloud data platform migrations — translating DB2 / NPS physical models to cloud-native equivalentsFamiliarity with modern data stack modelling tools (dbt) and their integration with traditional enterprise data modelling frameworksBackground in agile delivery, DevOps practices, and CI/CD pipelines for data model versioning and deploymentMaster’s degree in Data Science, Information Systems, Computer Science, or a related field preferred #Remote