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Data Modeler - Azure Data Engineering

India

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Data Modeler – Azure Data Engineering

Location: Hyderabad

Experience 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.