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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.