Data Modeler - Erwin/Metadata

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