Job Description

REQUIREMENTS:

• Bachelors degree in Computer Science, Information Systems, or a related field (preferred).
• Proven expertise in data modelling (Data Vault and Dimensional), data integrity principles, metadata management, and an understanding of data lineage.
• Experience in regulated environments (e.g., banking/financial services) with knowledge of risk reporting requirements, data governance frameworks, and privacy/security regulations.
• Strong analytical, communication, and collaboration skills, capable of working in agile, cross-functional teams and adapting to evolving business and regulatory conditions.

Competencies & Skills:

• Data Modelling Mastery (Conceptual, Logical & Physical)
• Commitment to Data Quality, Lineage, & Metadata Standards
• Compliance & Risk-Aware Mindset
• Agile & Collaborative Work Style
• Adaptability to Evolving Regulatory & Technological Landscapes

• Key Responsibilities:

Model Creation & Compliance:

• Develop conceptual, logical and physical data models that adhere to enterprise and data standards, incorporate data lineage, metadata definitions, and data quality controls, and align with governance directives and regulatory requirements.
• Ensure models support accurate, complete, and timely aggregation and reporting of risk-related data, reflecting the banks obligations to produce trusted, compliant, and transparent data outputs.

Collaboration & Documentation:

• Work closely with Data Architects to understand domain-level architectures, clarifying any ambiguities and ensuring models accurately reflect intended data flows and structures.
• Collaborate with Data Engineers to ensure that the models can be effectively implemented, scaled, and maintained, and that any performance, cost, or scalability considerations are addressed in the modelling stage.
• Produce thorough model documentation, including entity-relationship diagrams, data dictionaries, and metadata records, enabling easy reference, maintenance, and auditing of data models.

Continuous Improvement & Adaptability:

• Profile source data and analyse data quality issues, making adjustments to models to meet established standards for accuracy, consistency, and compliance.
• Take part in proofs of concept (POCs), pilot projects, and sandbox environments to test model designs, ensuring they remain robust, future-ready, and responsive to regulatory changes and emerging technologies in the data and analytics landscape.

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