Design, implement, and optimize enterprise data architecture and models that represent complex data using relational, dimensional, and NoSQL databases. Ensure data is structured for high performance, clarity, and long-term scalability across both operational and analytical systems. The Data Modeler will serve as the translator between business stakeholders and data engineering teams.
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Key Responsibilities
- Assessment: Review current data models to understand the AS-IS environment.
- Conceptual, Logical, and Physical Modeling: Plan, design, and maintain blueprints of the TO-BE data models for OLTP (operational), OLAP (analytical), and Enterprise Data Lakehouse environments and how data will be stored, managed, and used.
- Business Alignment: Understand business processes and translate them into robust data requirements while reviewing current report design documents to align with business needs.
- Multi-Paradigm Modeling: Design 3NF (Third Normal Form) for transactional databases, Dimensional Models (Kimball/Star Schema) for data warehousing, and Data Vault 2.0 for scalable enterprise data lakes.
- Data Quality: Transform unstructured data into structured data by fixing errors and eliminating redundancies to optimize data systems.
- Performance Optimization: Optimize query performance through smart indexing, partitioning, and materialization strategies.
- Data Governance: Establish, enforce, and implement best practices, data naming standards, data definitions, and maintain data maps and relevant diagrams for data systems and metadata management.
- Data Security: Specify rules and guidelines for ensuring data security and compliance, and define procedures for classifying data according to sensitivity.
- Tooling & Documentation: Maintain data schemas using modern standards modeling tools and ensure documentation is accessible to both technical and non-technical users.
Qualifications
- B.Sc. Degree with a minimum of 5+ years of dedicated data modeling experience in enterprise relational databases and columnar databases.
- SQL Proficiency: Write, read, and optimize complex SQL transactional and analytical queries.
- Modern Data Stack Familiarity: Experience modeling for cloud data warehouses/lakehouses (e.g., SQLSERVER, Hadoop, Snowflake, BigQuery, Databricks) alongside transformation tools (e.g., dbt), and creating ERDs for relational systems and dimensional diagrams.
- Tooling: Proficiency with ER/Studio, Erwin, or similar data modeling software.
- NoSQL/Unstructured Data: Understanding of how to model semi-structured data (JSON, Parquet) and NoSQL databases (MongoDB, Cassandra) alongside relational data.
- Physical Data Management: Understand current data storage constraints and define TO-BE needs with growth allocation.
- Data Integration: Familiarity with ETL tools to define the rules for data transformations and prepare data for big data analytics.
- Soft Skills: Strong communication and presentation skills, ability to identify business needs, and the capability to lead or mentor a team.
Certificates
- W3school SQL Certification
- DAMA-CDMP Certification
- Microsoft: Azure Data Engineer Associate
Level
Mid-senior level
Location
Riyadh, Saudi Arabia
