Job Description:
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Design and implement conceptual logical and physical data models to support business intelligence analytics and operational reporting.
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Develop dimensional models (star/snowflake schemas) for data warehouses and Lakehouses.
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Collaborate with data engineers analysts and business stakeholders to understand data requirements and translate them into robust data models.
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Utilize data modeling tools such as Erwin PowerDesigner or dbt to create and maintain models.
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Ensure data models align with enterprise architecture standards and support data governance privacy and compliance frameworks.
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Work with cloud platforms (AWS Azure or GCP) and modern data stack tools to implement scalable data solutions.
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Apply concepts of data mesh data vault modeling and data fabric to support decentralized and federated data architectures.
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Optimize SQL queries and ensure high performance of data models in platforms like Snowflake Databricks or other Lakehouse environments.
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Support machine learning and advanced analytics initiatives by ensuring data models are analytics ready.
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Document data models and metadata to facilitate understanding and usage across teams.
Job Description: Design and implement conceptual logical and physical data models to support business intelligence analytics and operational reporting. Develop dimensional models (star/snowflake schemas) for data warehouses and Lakehouses. Collaborate with data engineers analysts and busin...
Job Description:
-
Design and implement conceptual logical and physical data models to support business intelligence analytics and operational reporting.
-
Develop dimensional models (star/snowflake schemas) for data warehouses and Lakehouses.
-
Collaborate with data engineers analysts and business stakeholders to understand data requirements and translate them into robust data models.
-
Utilize data modeling tools such as Erwin PowerDesigner or dbt to create and maintain models.
-
Ensure data models align with enterprise architecture standards and support data governance privacy and compliance frameworks.
-
Work with cloud platforms (AWS Azure or GCP) and modern data stack tools to implement scalable data solutions.
-
Apply concepts of data mesh data vault modeling and data fabric to support decentralized and federated data architectures.
-
Optimize SQL queries and ensure high performance of data models in platforms like Snowflake Databricks or other Lakehouse environments.
-
Support machine learning and advanced analytics initiatives by ensuring data models are analytics ready.
-
Document data models and metadata to facilitate understanding and usage across teams.
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