Data Modeler - Azure Data Engineering

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.