Data Modeller

2 weeks ago

Johannesburg, Gauteng, South Africa Golden Rule Full time

Reporting Line:
Reports to the Data Architect Modeller Lead

Role Overview:
The Data Modeller transforms the conceptual and logical architectures provided by the Data Architect into implementable logical and physical data models. Operating within a data-driven, compliance-focused digital banking environment, the Data Modeller ensures all models adhere to data modelling standards, facilitate accurate and timely risk data aggregation, and meet the stringent data quality, lineage, and security requirements set by the governance and regulatory frameworks. The Data Modeller works closely with Data Architects, Data Engineers, the Data Governance team, and other stakeholders to ensure that the resulting data models are reliable, fit for advanced analytics (including AI/ML), and readily adaptable to evolving business and regulatory demands.

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 bank's 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.

What Success Looks Like:

  • Detailed, accurate, and well-documented data models that are easily implemented by Data Engineers, enabling accurate, timely risk data aggregation and advanced analytics applications.
  • Consistent adherence to governance, data quality, and security standards, resulting in trusted data assets that can withstand audits, comply with regulations, and meet business intelligence and machine learning needs.
  • Ongoing refinement and improvement of data models as requirements, regulatory conditions, and technology capabilities evolve.

Requirements Qualifications:

  • Bachelor's 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) 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

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