As a Data Modeler at TVH , you will play a central role in shaping how our global organization structures, understands, and leverages its data. Operating across all core business domains (e.g., Customer, Supplier, Item, Product, Equipment, HR, Finance, Commercial, Operations, …), you will design, develop, and maintain high-performing Logical Data Models (LDM) and Physical Data Models (PDM) that power enterprise reporting, analytics, and operational applications.
You will translate diverse cross-domain business requirements into clear, standardized, and scalable data structures.
Working closely with Domain Architects, Data Engineers, and Data Stewards, you will ensure our logical abstractions align perfectly with business reality while our physical implementations maximize database performance and data accessibility.
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About The Role
As a Data Modeler at TVH , you will play a central role in shaping how our global organization structures, understands, and leverages its data. Operating across all core business domains (e.g., Customer, Supplier, Item, Product, Equipment, HR, Finance, Commercial, Operations, …), you will design, develop, and maintain high-performing Logical Data Models (LDM) and Physical Data Models (PDM) that power enterprise reporting, analytics, and operational applications.
You will translate diverse cross-domain business requirements into clear, standardized, and scalable data structures.
Working closely with Domain Architects, Data Engineers, and Data Stewards, you will ensure our logical abstractions align perfectly with business reality while our physical implementations maximize database performance and data accessibility.
Key Responsibilities
Enterprise Logical & Physical Data Model Design: Design, construct, and maintain normalized Conceptual and Logical Data Models (LDM) across all enterprise domains, establishing clear entities, attributes, relationships, and business definitions. Translate LDMs into optimized Physical Data Models (PDM) tailored for target database platforms, balancing storage efficiency, index strategies, and query performance. Reverse-engineer legacy source systems and translate complex domain requirements into normalized, reusable data structures. Produce and manage comprehensive data documentation, including ERDs (Entity-Relationship Diagrams), data dictionaries, and data mapping lineage.
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Design, construct, and maintain normalized Conceptual and Logical Data Models (LDM) across all enterprise domains, establishing clear entities, attributes, relationships, and business definitions.
Translate LDMs into optimized Physical Data Models (PDM) tailored for target database platforms, balancing storage efficiency, index strategies, and query performance.
Reverse-engineer legacy source systems and translate complex domain requirements into normalized, reusable data structures.
Produce and manage comprehensive data documentation, including ERDs (Entity-Relationship Diagrams), data dictionaries, and data mapping lineage.
Domain Alignment & Standards Governance: Drive consistent data modeling standards, naming conventions, and design patterns across all domain modeling initiatives. Partner with Data Governance teams to incorporate standardized business glossaries and enterprise metadata standards directly into model structures. Conduct data model reviews with technical and business teams to ensure cross-domain consistency and avoid data duplication.
Drive consistent data modeling standards, naming conventions, and design patterns across all domain modeling initiatives.
Partner with Data Governance teams to incorporate standardized business glossaries and enterprise metadata standards directly into model structures.
Conduct data model reviews with technical and business teams to ensure cross-domain consistency and avoid data duplication.
Cross-Functional Technical Collaboration: Work hand-in-hand with Domain Architects to ensure domain models fit seamlessly into TVH’s target data architecture strategy. Collaborate with Data Engineers to guide the physical realization of models and support optimized ETL/ELT pipeline design. Partner with Data Analysts, Data Stewards and Business Partners to validate logical assumptions, clarify business logic, and refine domain models as needs evolve.
Work hand-in-hand with Domain Architects to ensure domain models fit seamlessly into TVH’s target data architecture strategy.
Collaborate with Data Engineers to guide the physical realization of models and support optimized ETL/ELT pipeline design.
Partner with Data Analysts, Data Stewards and Business Partners to validate logical assumptions, clarify business logic, and refine domain models as needs evolve.
Model Optimization & Quality: Profile source data using SQL to uncover structural edge cases and validate cardinality before finalizing logical designs. Refine physical schemas for cloud data warehouses, adjusting partitioning, clustering, and denormalization strategies to meet performance SLAs.
Profile source data using SQL to uncover structural edge cases and validate cardinality before finalizing logical designs.
Refine physical schemas for cloud data warehouses, adjusting partitioning, clustering, and denormalization strategies to meet performance SLAs.
Qualifications
5+ years of hands-on experience focused specifically on Logical Data Modeling (LDM) and Physical Data Modeling (PDM) across multiple business domains.
Deep expertise in diverse modeling paradigms: Relational/Entity-Relationship (3NF), Dimensional Modeling (Star/Snowflake), and Denormalization concepts.
Strong proficiency in SQL for deep source system profiling, data mapping, and schema validation.
Hands-on experience with modern cloud data warehouses (preferably GCP BigQuery , Snowflake, or Databricks) and relational database platforms.
Proficiency with enterprise data modeling tools (e.g., Erwin, PowerDesigner, SqlDBM, or similar).
Solid understanding of Data Warehousing principles, metadata management, and ETL/ELT architecture.
Fluent in English with excellent communication skills to articulate complex logical structures to both business stakeholders and engineering teams.
Preferred Skills
Experience with Data Vault 2.0 modeling methodologies for enterprise scale.
Exposure to real-time or streaming data modeling patterns (Event-driven architectures).
Understanding of data privacy impact on data design (e.g., handling PII attributes across global domains).
Experience working in Agile/Scrum distributed software delivery teams.
