Job Description

Job Purpose:

The Senior Data Modeler is responsible for designing and maintaining robust data models that support the organizations data architecture. This role involves collaborating with various stakeholders to ensure that data structures effectively meet business needs and facilitate efficient data management and analytics.

Role and Responsibilities

• Data Modeling:
  Design, develop, and maintain logical and physical data models that align with business requirements and data governance standards.
• Collaboration: Work closely with data architects, data engineers, and business analysts to gather requirements and translate them into data models.
• Data Quality Assurance:
  Ensure data integrity and quality by establishing standards and best practices for data modeling.
• Documentation:
  Create and maintain comprehensive documentation of data models, dealing with BRD documents, data dictionaries, and metadata.
• Performance Optimization: Monitor and optimize data model performance to facilitate efficient data retrieval and processing.
• Mentorship: Provide guidance and support to junior data modelers and team members on best practices and methodologies.

Qualifications and Education Requirements

• Bachelors degree in Computer Engineering, Computer Science, or Information Technology.
• 7+ years of hands-on experience with relational, dimensional, and/or analytic data modeling using RDBMS, dimensional, and NoSQL data platform technologies, as well as ETL and data ingestion protocols.
• Strong understanding of database management systems, data warehousing concepts and different architecture & modeling techniques.
• Fluent in English and Arabic

Preferred Skills

• Proficiency in data modeling tools (e.g., Erwin, PowerDesigner, IBM Infosphere, Microsoft Visio).
• experience with SQL, NoSQL, semi-SQL databases and cloud storage.
• Experience with Architecure Design and data warehouse solutions (e.g., BigQuery, Snowflake, Redshift, Azure Synapse).
• Experience in cloud architecture design (e.g., Google Cloud, AWS, Azure).
• Familiarity with data integration and ETL processes.
• Knowledge of data governance and data quality frameworks.
• Strong analytical and problem-solving skills.