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About the role
As an experienced Enterprise Data Modeler specializing in Supply Chain Management, your role will primarily involve designing and maintaining enterprise-level data models to support various supply chain operations, analytics, and decision-making processes. Key Responsibilities: - Develop and maintain enterprise data models that are well-aligned with supply chain processes such as procurement, manufacturing, logistics, inventory management, and distribution. - Collaborate effectively with business stakeholders to translate supply chain requirements into comprehensive logical and physical data models. - Ensure the integrity, consistency, and scalability of data across all enterprise systems. - Work closely with data architects, engineers, and analysts to successfully implement models using platforms like Snowflake, SAP, or other enterprise solutions. - Provide support for data governance initiatives, including metadata management and master data alignment. - Optimize data structures to enhance reporting, analytics, and facilitate advanced use cases like AI/ML and predictive supply chain modeling. - Document data definitions, relationships, and standards to ensure seamless enterprise-wide adoption. Must-Have Skills: - Demonstrate strong expertise in various data modeling techniques including conceptual, logical, and physical models. - Hands-on experience in handling supply chain data domains such as orders, shipments, inventory, vendor management, and demand planning. - Proficiency in SQL and working knowledge of data warehouse platforms like Snowflake, Redshift, SAP HANA, or similar technologies. - Practical experience with ETL/ELT tools such as Matillion, dbt, etc. - Knowledge of data governance frameworks and best practices. Good-to-Have Skills: - Exposure to supply chain planning tools like SAP IBP, Kinaxis, Blue Yonder. - Familiarity with cloud platforms such as AWS, Azure, GCP. - Experience with data visualization tools like Power BI, Tableau. - Understanding of AI/ML applications in the context of supply chain analytics. As an experienced Enterprise Data Modeler specializing in Supply Chain Management, your role will primarily involve designing and maintaining enterprise-level data models to support various supply chain operations, analytics, and decision-making processes. Key Responsibilities: - Develop and maintain enterprise data models that are well-aligned with supply chain processes such as procurement, manufacturing, logistics, inventory management, and distribution. - Collaborate effectively with business stakeholders to translate supply chain requirements into comprehensive logical and physical data models. - Ensure the integrity, consistency, and scalability of data across all enterprise systems. - Work closely with data architects, engineers, and analysts to successfully implement models using platforms like Snowflake, SAP, or other enterprise solutions. - Provide support for data governance initiatives, including metadata management and master data alignment. - Optimize data structures to enhance reporting, analytics, and facilitate advanced use cases like AI/ML and predictive supply chain modeling. - Document data definitions, relationships, and standards to ensure seamless enterprise-wide adoption. Must-Have Skills: - Demonstrate strong expertise in various data modeling techniques including conceptual, logical, and physical models. - Hands-on experience in handling supply chain data domains such as orders, shipments, inventory, vendor management, and demand planning. - Proficiency in SQL and working knowledge of data warehouse platforms like Snowflake, Redshift, SAP HANA, or similar technologies. - Practical experience with ETL/ELT tools such as Matillion, dbt, etc. - Knowledge of data governance frameworks and best practices. Good-to-Have Skills: - Exposure to supply chain planning tools like SAP IBP, Kinaxis, Blue Yonder. - Familiarity with cloud platforms such as AWS, Azure, GCP. - Experience with data visualization tools like Power BI, Tableau. - Understanding of AI/ML applications in the context of supply chain analytics.