Job Purpose
The Data Modeler & Architect is responsible for designing, developing, the bank's enterprise data architecture and data models to support operational systems, analytics, and strategic decision-making. Partners with business stakeholders, data engineers, and technology team to translate business requirements into robust data designs, establish and enforce data standards, and guide the implementation of secure, high-quality, and maintainable data solutions.
Key Responsibilities & Accountabilities
- Define, develop and maintain conceptual, logical, and physical data models aligned with business and regulatory requirements.
- Establish data architecture principles, standards, and best practices across the bank.
- Provide architectural guidance for data platforms, data integration solutions, and analytics environments
- Collaborate closely with Data Governance, Data Engineers, IT, and Business teams to ensure high-quality data design and delivery.
- Implement data modeling best practices and contribute to enterprise data standards.
- Support data warehousing, metadata management, and data quality improvement initiatives.
- Translate complex business needs into robust and scalable data structures.
- Evaluate and recommend data management technologies, tools, and frameworks.
Qualification & Experience
- Minimum 7 years of proven experience in data modeling within large-scale enterprise environments.
- Strong expertise in ER modeling, dimensional modeling, and data warehousing concepts.
- Proficiency with industry-standard modeling tools (e.g., ERwin, PowerDesigner, or equivalent).
- Solid understanding of data governance frameworks, data architecture, and regulatory data requirements.
- Comprehensive knowledge of SAMA regulations and NDMO data standards, with ability to model data in line with local regulatory expectations.
- Proven experience in the banking or financial services sector is required.
- Excellent analytical and communication skills.
Competencies
- Enterprise Data Architecture Ability to design, implement, and maintain scalable enterprise data architectures across operational, analytical, cloud, and hybrid environments.
- Data Modeling Mastery Expertise in conceptual, logical, and physical data modeling using industry-standard tools and best practices.
- Database & Storage Systems Strong knowledge of relational, NoSQL, and cloud-native databases and the ability to optimize structures for performance and scalability.
- Data Integration & Pipelines Skilled in ETL/ELT design, data pipeline orchestration, and integration patterns used in modern data platforms.
- Cloud & Modern Data Platforms Proficient with cloud ecosystems (AWS, Azure, GCP), data lakes, and data warehouse technologies.
- Data Governance & Quality Solid understanding of metadata, lineage, MDM, data quality controls, and standardization frameworks.
- Security & Compliance Knowledge of data privacy, security protocols, and regulatory compliance requirements (NDMO, PDPL).
Skills
- Data modeling (conceptual, logical, physical)
- Data architecture design
- Database design & optimization
- SQL (advanced querying & performance tuning)
- ETL/ELT development
- Data warehouse, data lake design
- Metadata management & data cataloging
- Master data management (MDM)
- Data quality and validation frameworks
- Data integration patterns (batch, streaming, APIs)
- Data governance implementation
- Data lineage and impact analysis
- Modeling tools (ERwin, PowerDesigner, SQL DBM, etc.)
