Data Modeler

5 - 9 years

0 Lacs

Posted:1 day ago| Platform: Shine logo

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Work Mode

On-site

Job Type

Full Time

Job Description

As a Data Modeler specializing in Hybrid Data Environments, you will play a crucial role in designing, developing, and optimizing data models that facilitate enterprise-level analytics, insights generation, and operational reporting. You will collaborate with business analysts and stakeholders to comprehend business processes and translate them into effective data modeling solutions. Your expertise in traditional data stores such as SQL Server and Oracle DB, along with proficiency in Azure/Databricks cloud environments, will be essential in migrating and optimizing existing data models. Your responsibilities will include designing logical and physical data models that capture the granularity of data required for analytical and reporting purposes. You will establish data modeling standards and best practices to maintain data architecture integrity and collaborate with data engineers and BI developers to ensure data models align with analytical and operational reporting needs. Conducting data profiling and analysis to understand data sources, relationships, and quality will inform your data modeling process. Your qualifications should include a Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field, along with a minimum of 5 years of experience in data modeling. Proficiency in SQL, familiarity with data modeling tools, and understanding of Azure cloud services, Databricks, and big data technologies are essential. Your ability to translate complex business requirements into effective data models, strong analytical skills, attention to detail, and excellent communication and collaboration abilities will be crucial in this role. In summary, as a Data Modeler for Hybrid Data Environments, you will drive the development and maintenance of data models that support analytical and reporting functions, contribute to the establishment of data governance policies and procedures, and continuously refine data models to meet evolving business needs and leverage new data modeling techniques and cloud capabilities.,

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