Data Modeling Specialist

We are seeking an experienced Data Modeling Specialist to design, develop, and maintain enterprise data models that support modern analytics, reporting, AI, and operational data solutions. This role will be responsible for creating and governing conceptual, logical, and physical data models across cloud and on-premise platforms, including Azure Synapse Analytics, Oracle, and Databricks. The ideal candidate will possess deep expertise in data architecture, data modeling standards, metadata management, data lineage, and governance practices. The role will partner closely with Data Engineers, Solution Architects, Business Analysts, Governance teams, and Data Product Owners to ensure consistent, scalable, and business-aligned data designs. Key Responsibilities Data Modeling:Design and maintain enterprise conceptual, logical, and physical data models. Develop dimensional, normalized, and hybrid data models supporting analytics and operational workloads. Translate business requirements into scalable and maintainable data structures. Create subject area models, canonical data models, and data domain structures. Ensure alignment of data models with enterprise architecture and business capabilities. Platform-Specific Data Design Azure Synapse Analytics:Design dimensional and reporting models optimized for Synapse dedicated and serverless environments. Define partitioning, distribution, and performance optimization strategies. Support data warehouse and lakehouse architectures. Databricks:Model data structures supporting Lakehouse architectures. Design Bronze, Silver, and Gold layer data models. Support Delta Lake implementations and data product development. Collaborate with Data Engineering teams to optimize model performance and scalability. Oracle:Design and maintain normalized operational data models. Support transactional and analytical databases. Define indexing, partitioning, and performance optimization standards. Collaborate with database administrators to ensure efficient physical database implementations. Data Mapping and Integration:Create and maintain source-to-target mapping documentation. Define transformation rules and business logic for data integration solutions. Support data migration, modernization, and cloud transformation initiatives. Work with Data Engineers to ensure accurate implementation of data models and mappings. Validate data integrity across inbound and outbound interfaces. Data Governance and Metadata Management:Support enterprise data governance initiatives. Define and maintain business and technical metadata. Establish and document data standards and naming conventions. Support data stewardship activities and governance reviews. Ensure compliance with enterprise data management policies and regulatory requirements. Data Lineage and Data Quality:Document end-to-end data lineage from source systems through consumption layers. Support implementation of lineage management tools and processes. Partner with governance teams to improve data transparency and traceability. Define data quality rules, standards, and validation requirements. Assist in root-cause analysis of data quality issues. Collaboration and Leadership:Partner with Business Analysts, Data Architects, Data Engineers, and Solution Architects. Participate in architecture reviews and solution design workshops. Provide guidance on data modeling best practices and standards. Mentor junior team members in data architecture and modeling disciplines. Promote data management and governance best practices across the organization.