Data Architect

4 weeks ago

Hyderabad, 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 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.

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