Data Modeler

19 hours ago

New Delhi, India Tezo Full time

Tezo is a new generation Digital & AI solutions provider, with a history of creating remarkable outcomes for our customers. We bring exceptional experiences using cutting-edge analytics, data proficiency, technology, and digital excellence.Data Modeler – Azure Data EngineeringLocation: HyderabadExperience Level: 8–13 Years- 12+ years of experience in data management and data architecture, including 5+ years focused on data modeling. - Expertise in dimensional modeling (Star/Snowflake), normalized models, and data vault / data lake modeling. - Strong experience with SQL and Azure Cloud ecosystem — Azure Synapse, Data Factory, Data Lake, SQL DB, Databricks. - Proven experience designing data models for enterprise data warehouses, data lakes, and analytics platforms. - Experience working with business glossary, metadata management, and data catalog tools (e.g., Purview, Collibra). - Knowledge of ETL/ELT processes, data pipelines, and data integration patterns. - Excellent communication, stakeholder management, and documentation skills.Key Responsibilities- Data Modelling & Architecture - Design, develop, and maintain conceptual, logical, and physical data models for data warehouse, data lakehouse, and transactional systems. - Implement and optimize dimensional models (star/snowflake) and data vault/lakehouse models aligned with business needs. - Define and enforce data modeling standards, naming conventions, and metadata management practices. - Collaborate with architects to define the data architecture blueprint, ensuring scalability, governance, and performance. - Cloud Data Engineering (Azure) - Partner with Azure data engineers to implement data models using services such as Azure Data Lake, Azure Synapse Analytics, Azure SQL Database, Data Factory, and Databricks. - Contribute to the design and optimization of data ingestion, transformation, and orchestration pipelines in Azure. - Participate in data governance, master data management (MDM), and data quality initiatives. - Collaboration & Stakeholder Engagement - Work with business teams and data analysts to understand reporting and analytical requirements. - Partner with enterprise architects to align modeling practices with data strategy and enterprise standards. - Document data models, lineage, and definitions using enterprise metadata tools.

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