Key Responsibilities:

- Design, develop, and maintain enterprise-level conceptual, logical, and physical data models.

- Create scalable data architectures using Data Vault and dimensional modeling methodologies.

- Develop and optimize data models to support analytics, reporting, and business intelligence requirements.

- Collaborate with data architects, engineers, analysts, and business stakeholders to understand data requirements.

- Define data entities, relationships, attributes, and business rules.

- Create and maintain data model documentation, standards, and governance guidelines.

- Use Erwin Data Modeler for designing, maintaining, and documenting data models.

- Support data warehouse and data lake implementations.

- Work with modern data technologies such as Hive, Trino, Iceberg, and Snowflake Cortex.

- Analyze existing data structures and recommend improvements for performance and scalability.

- Ensure data models align with enterprise data governance and quality standards.

- Participate in data architecture discussions and provide modeling recommendations.

Required Skills:

- Strong hands-on experience in Data Modeling concepts and methodologies.

- Expertise in Data Vault modeling (Hub, Link, Satellite concepts).

- Strong experience in Dimensional Modeling including Star Schema and Snowflake Schema design.

- Hands-on experience with Erwin Data Modeler.

- Experience designing logical and physical data models for enterprise data platforms.

- Good understanding of relational database concepts and data warehouse architecture.

- Experience working with large-scale data environments and analytical platforms.

- Knowledge of data integration patterns and data lifecycle management.

- Strong analytical, problem-solving, and communication skills.

Preferred Skills:

- Experience working with Hive and Trino query engines.

- Knowledge of Apache Iceberg table formats and data lake architectures.

- Experience with Snowflake data platform and Snowflake Cortex capabilities.

- Understanding of cloud-based data platforms and modern data architecture patterns.

- Experience with ETL/ELT processes and data engineering workflows.

- Knowledge of data governance, metadata management, and data quality frameworks.

- Familiarity with Agile/Scrum methodologies.