Data Modelling+ Data Vault

This is a remote position. Data Modeler

• Data Vault Location: Bangalore Job Type: Full-Time Primary Responsibilities: As a Data Modeler, you will be responsible for: Data Vault Modeling: Utilizing your expertise in Data Vault modeling approach to design, implement, and document data architecture solutions. Snowflake: Demonstrating extensive knowledge of Snowflake, a key component of our data ecosystem. Data Modeling: Creating conceptual, logical, and physical data models that align with business objectives and data architecture standards. Platform Implementation: Implementing relational databases, operational data stores (ODS), data marts, and data lakes on target platforms. Standards and Best Practices: Defining and governing data modeling and design standards, tools, and best practices for enterprise data models. Hands-On Expertise: Hands-on modeling, design, configuration, installation, and performance tuning of data models. Metadata Management: Managing metadata effectively and using data modeling tools such as Erwin or ER Studio. Collaboration: Working closely with database engineers to create optimal physical data models for datasets. Alignment: Interacting with subject matter experts (business) and architects to ensure alignment between data models and technology recommendations for projects. Secondary Responsibilities: In addition to your primary role, you should have: TSQL Knowledge: Good knowledge of Transact-SQL (TSQL) for efficient data querying and manipulation.

Qualifications

To succeed in this role, you should possess the following qualifications: Education: A Bachelor's or Master's degree in Computer Science, Information Technology, or a related field. Experience: Proven experience in Data Vault modeling and Snowflake is essential. Skills: Strong skills in data modeling, database design, and metadata management.

Proficiency in data modeling tools like Erwin or ER Studio is preferred. Communication: Excellent communication and collaboration skills to work effectively with cross-functional teams. Problem-Solving: Strong analytical and problem-solving abilities to address complex data modeling challenges. Adaptability: Ability to adapt to evolving data technologies and industry best practices.