Data Modeler

Qualifications:

·   10+ years of experience in Data Warehousing, Data modeling, Database design.

·   Should have worked in designing & building cloud-based data warehouses, databases and data models in Azure, Databricks.

·   Should have worked in data modelling tools such as Erwin, ER Studio, etc.

·   Should have proficiency in data modelling for transactional, Operational reporting, and analytical solutions (EDW, Data Lake)

·   Should have strong hands-on experience writing complex SQL analytical queries (T-SQL, Spark SQL)

·   Should have strong hands-on experience in optimizing the data models, SQL queries, databases for performance and scalability.

·   Should have in depth knowledge of designing data models using data in Databricks, Azure Synapse, Azure SQL, SQL Server.

·   Nice to have knowledge of: Azure Data Factory, SSIS, Azure Functions, Logic Apps, Azure Event Hubs, PySpark, Spark Streaming, ADLS Gen2, Azure Blob, and Tableau.

·   Should have worked with Agile methodology.

·   Should have worked on handling Batch, Event & Streaming data.

·   Should have experience in re-modeling of legacy application databases. 

·   Should have excellent communication, client-facing, problem-solving skills

·   Should have working experience in a global delivery model

·   Should have experience in working with product managers, project managers, business users, applications development team members, DBA teams and Data Governance team on a daily basis to analyze requirements, design, development and deployment technical solutions



Responsibilities:

Collaborate with stakeholders to understand business requirements and translate them into data models that support data-driven decision-making and business objectives.
Design, develop, and maintain conceptual, logical, and physical data models that accurately represent the organization's data assets.
Evaluating existing data systems and ensure data models facilitate interoperability and data exchange across disparate systems and platforms.
Implement best practices for data modeling, including normalization, denormalization, and optimization techniques to ensure data integrity and efficiency.
Work closely with database administrators and developers to implement data models into database systems efficiently.