Data Modeler

Requirements:

• 5+ years of proven experience as a Data Modeler in complex data environments.
• Strong expertise in Azure Data Fabrics, Azure Data Lake, and Azure Data Factory (ADF) for ETL processes.
• Experience with Oracle E-Business Suite/Fusion and Microsoft CRM as source systems.
• Solid understanding of data warehousing concepts, star/snowflake schemas, and dimensional modelling.
• Familiarity with data governance practices and tools such as Azure Purview.
• Excellent problem-solving skills and ability to work independently and collaboratively.
• Strong communication skills to interact effectively with technical and non-technical stakeholders.
• Leverage Azure OpenAI, ML models, and Copilot for advanced analytics.
• Enable real-time data processing with Event Streams and KQL databases.
• Support data-driven AI applications by structuring data for efficient consumption.
• Microsoft Azure certifications (e.g., Azure Data Engineer, Azure Solutions Architect) are a plus.
• Experience with Power BI or other BI tools for reporting and visualization.
• Knowledge of Azure Synapse Analytics and Databricks for advanced analytics.
• Familiarity with Agile methodologies and CI/CD practices for data projects.

Responsibilities:

• Design and develop data models (conceptual, logical, and physical) to support reporting, analytics, and data integration initiatives.
• Should have good experience in any of Data Modelling Tools like Erwin, Visio, dbForge, Power Designer
• Help data engineers for data pipelines using Azure Data Factory (ADF) to extract, transform, and load (ETL) data from various source systems.
• Leverage Azure Fabrics/Data Lake for data storage, management, and processing.
• Integrate data from Oracle E-Business Suite/Fusion and Microsoft CRM systems into Azure Data Lake and other cloud-based platforms.
• Ensure data quality, consistency, and governance throughout the data lifecycle.
• Collaborate with business analysts, data engineers, and stakeholders to gather and translate requirements into scalable data models.
• Optimize data models for performance and scalability to handle future data requirements.
• Develop and enforce best practices for data architecture, ETL processes, and data governance.
• Perform data profiling and analysis to identify and resolve data quality issues.
• Create and maintain comprehensive data documentation and metadata management.