Data Modeler - Azure Data Engineering

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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.