Data Modeler - ER/Studio

Role Overview

Key Responsibilities

Data Model Design :

• Develop conceptual, logical, and physical data models aligned with business needs and enterprise architecture standards.
• Implement RDBMS, operational data stores (ODS), data marts, and data lakes on target SQL/NoSQL platforms.
  

Data Architecture & Integration

• Collaborate with data engineers, solution architects, and business analysts to ensure seamless integration between data models and ETL/ELT pipelines.
• Design models optimized for reporting, analytics, and big data processing.
  

Metadata & Governance

• Maintain and document data dictionaries, metadata repositories, and modeling standards.
• Ensure compliance with enterprise data governance and security policies.
  

Specialized Modeling

• Design and implement Data Vault 2.0 models for scalability and historical data tracking.
• Leverage industry best practices for data warehousing and enterprise big data platforms across multidata-center deployments.
  

Collaboration & Review

• Conduct data model reviews with stakeholders and ensure models meet business, performance, and scalability requirements.
• Provide recommendations for data model optimization and refactoring.
  

Required Skills & Experience

Core Skills :

• Strong hands-on experience in data modeling (conceptual, logical, and physical).
• Proficiency in SQL and NoSQL database platforms.
• Solid understanding of RDBMS, ODS, data marts, and data lakes architecture.
  

Tools & Platforms

• Proficiency with Erwin Data Modeler, ER/Studio, or equivalent modeling tools.
• Familiarity with cloud-based data platforms (AWS, Azure, GCP) is a plus.
  

Specialized Expertise

• Minimum 5 years of data modeling experience.
• Minimum 2 years of Data Vault modeling experience.
• Strong understanding of metadata management, data warehousing, and enterprise big data solutions.
  

Soft Skills

• Strong analytical, problem-solving, and documentation skills.
• Excellent communication skills to interact with business and technical teams.
  

Preferred Qualifications

• Experience with multi-data-center data platform deployments.
• Exposure to dimensional modeling and star/snowflake schemas.
• Familiarity with ETL/ELT processes and data quality frameworks.
• Knowledge of data governance and compliance frameworks (e.g., GDPR, HIPAA).