Senior Data Modeler Architect

Position Summary

We are seeking an experienced Data Modeler to design, develop, and maintain scalable enterprise data models supporting analytics, reporting, operational applications, data warehousing, and data integration initiatives.

The Data Modeler will collaborate closely with business stakeholders, Data Architects, Data Engineers, analysts, application teams, and governance teams to translate complex business requirements into well-defined conceptual, logical, and physical data models.

The ideal candidate will have strong hands-on experience in enterprise data modeling, relational and dimensional modeling, data warehousing, cloud data platforms, metadata management, and data governance. The role requires the ability to establish consistent data definitions, relationships, standards, and modeling practices across multiple business domains and technology platforms.

Key Responsibilities

Enterprise Data Modeling

• Design, develop, and maintain conceptual, logical, and physical data models for enterprise data platforms, analytics solutions, and business applications.
• Translate complex business requirements, business processes, and data requirements into scalable and reusable data structures.
• Develop and maintain:
• Relational data models
• Dimensional data models
• Star schemas
• Snowflake schemas
• Domain-oriented data models
• Define entities, attributes, relationships, primary keys, foreign keys, business keys, hierarchies, and reference-data structures.
• Design models that appropriately support transactional, analytical, historical, and reporting requirements.
• Perform model normalization and denormalization based on application, integration, analytics, and performance requirements.

Data Warehousing & Analytics Modeling

• Design dimensional models for enterprise data warehouses, data marts, and modern analytics platforms.
• Define facts, dimensions, measures, hierarchies, and grain based on business and analytical requirements.
• Design appropriate approaches for maintaining historical information, including Slowly Changing Dimensions (SCDs).
• Ensure models support reporting, BI, advanced analytics, and downstream data-consumption requirements.
• Partner with Data Engineers to ensure models can be effectively implemented within ETL/ELT and data-processing pipelines.

Data Standards & Governance

• Define and enforce enterprise data-modeling standards, naming conventions, data types, and modeling best practices.
• Partner with business and technical stakeholders to establish consistent definitions for critical business data.
• Support master data, reference data, metadata management, and data-governance initiatives.
• Identify authoritative data sources and help establish consistent definitions and relationships across enterprise systems.
• Contribute to the development and maintenance of business glossaries and data dictionaries.
• Participate in architecture and model design reviews to ensure proposed solutions comply with enterprise architecture and governance standards.

Metadata, Lineage & Documentation

• Maintain comprehensive documentation for conceptual, logical, and physical data models.
• Document:
• Business definitions
• Data entities and attributes
• Source-to-target relationships
• Data lineage
• Business rules
• Data ownership
• Transformation requirements
• Maintain model metadata using enterprise modeling and/or data-cataloging platforms.
• Ensure appropriate traceability between business requirements, source systems, data models, transformation logic, and downstream consumption.

Data Quality & Integrity

• Contribute to data-quality frameworks by identifying:
• Mandatory and optional data elements
• Valid relationships
• Referential-integrity requirements
• Data constraints
• Validation rules
• Business rules
• Work with Data Engineering and Data Governance teams to identify and resolve data-quality and consistency issues.
• Ensure models appropriately support auditability, historical tracking, lineage, and data traceability.
• Identify data redundancies, inconsistencies, and opportunities for standardization and reuse.

Required Skills & Experience

• 12+ years of experience in data management, data architecture, data warehousing, or related disciplines, with significant hands-on data-modeling experience.
• 7+ years of strong experience developing conceptual, logical, and physical data models.
• Deep understanding of:
• Relational data modeling
• Dimensional modeling
• Star and snowflake schemas
• Normalization and denormalization
• Entity-relationship modeling
• Data warehousing concepts
• Strong understanding of facts, dimensions, hierarchies, grain, surrogate keys, business keys, and Slowly Changing Dimensions.
• Strong experience defining entities, attributes, relationships, cardinality, primary/foreign keys, and referential-integrity rules.
• Experience translating complex business requirements into enterprise data structures and models.
• Strong knowledge of SQL and relational database concepts.
• Experience with modern cloud data platforms, data lakes, data warehouses, or lakehouse architectures.
• Understanding of ETL/ELT processes and data-integration patterns.
• Experience with metadata management, data lineage, data dictionaries, and business glossaries.
• Good understanding of Master Data Management (MDM), reference data, data quality, and data governance.
• Experience working with enterprise architecture, Data Engineering, analytics, and application-development teams.

Data Modeling Tools

Hands-on experience with one or more enterprise data-modeling tools such as:

• ER/Studio
• ERwin Data Modeler
• SAP PowerDesigner
• Visio
• Lucidchart
• Or equivalent enterprise modeling platforms