Director/Executive Director, Enterprise Data Modelling

Responsibilities:

Enterprise Data Modelling Leadership

• Define and maintain enterprise conceptual, logical, physical, canonical, dimensional, and semantic data models across business domains.

• Establish and govern data modelling standards, methodologies, naming conventions, model lifecycle practices, and reusable design patterns.

• Drive and support the phased migration towards, and ongoing development of, SMBC's global integrated data model, including the assessment, harmonisation, mapping, transition, and evolution of existing regional and domain models.

• Lead the global definition and harmonisation of common business entities, relationships, hierarchies, classifications, identifiers, and reference structures, while accommodating approved regional and local requirements.

Business and Stakeholder Engagement

• Partner with senior business and technology stakeholders to understand business processes, information requirements, and strategic priorities.

• Plan and facilitate workshops to harmonise definitions, resolve structural inconsistencies, and establish common understanding across functions.

• Translate complex business concepts and requirements into scalable, implementable, and governed data structures.

• Influence stakeholders and delivery teams to adopt enterprise modelling standards through clear rationale, pragmatic guidance, and effective decision-making.

Data Architecture and Platform Alignment

• Partner with Data Architecture and Data Engineering teams to ensure approved models are effectively implemented across platforms and applications.

• Define canonical models, schemas, and data contracts that support reliable integration across systems and data domains.

• Ensure data structures align with enterprise architecture principles, data product requirements, and long-term scalability needs.

• Define modelling requirements and transition patterns to support migration from existing regional and domain structures towards the global integrated data model, including schema evolution, version control, compatibility, and model lifecycle management.

Metadata, Semantics, and Reuse

• Define and maintain metadata requirements, data dictionaries, modelling documentation, and traceability between business concepts and technical structures.

• Design semantic models and business-friendly data layers that enable consistent reporting, analytics, AI, and self-service consumption.

• Establish traceability between agreed business definitions, data products, source data, transformation logic, and consumption models.

• Promote reusable modelling patterns and shared enterprise information assets to reduce duplication and accelerate delivery.

Governance, Quality, and Assurance

• Collaborate with Data Governance teams to define data quality requirements, critical data attributes, lineage requirements, ownership, and stewardship responsibilities.

• Review and provide assurance over strategic modelling decisions to confirm alignment with enterprise standards and approved architecture.

• Ensure models support applicable regulatory, risk, audit, control, and reporting requirements.

• Drive continuous improvement of modelling methods, tools, documentation, and adoption practices.

Delivery and Leadership

• Independently lead complex enterprise modelling initiatives from discovery and requirements analysis through design, implementation support, and ongoing maintenance.

• Ensure the creation and maintenance of high-quality modelling artefacts, contributing directly where appropriate while providing technical leadership, coaching, and direction to relevant teams and practitioners.

• Balance enterprise consistency with pragmatic delivery needs, documenting decisions, exceptions, and approved local extensions.

• Provide thought leadership on evolving modelling practices for modern analytics, data products, AI, and cloud data platforms.

Requirements:

• Typically 12 or more years of relevant experience in enterprise data modelling, information architecture, data architecture, or related roles within large and complex organisations.

• Demonstrated expertise in conceptual, logical, physical, canonical, dimensional, and semantic data modelling.

• Proven ability to personally develop enterprise and domain data models, data dictionaries, schemas, and supporting documentation.

• Strong stakeholder engagement, workshop facilitation, requirements analysis, negotiation, and conflict-resolution capabilities.

• Experience defining modelling standards and driving adoption across multiple domains, teams, and delivery initiatives.

• Strong understanding of metadata management, business glossaries, master and reference data, data lineage, data quality, and model governance.

• Experience supporting operational, analytical, regulatory, and AI-related data requirements.Ability to operate with a high degree of autonomy, manage competing priorities, and influence senior technical and non-technical stakeholders.

Preferred Qualifications

• Experience in banking, financial services, insurance, or another highly regulated industry.

• Familiarity with Azure Databricks and modern lakehouse concepts is an advantage.

• Familiarity with enterprise ER modelling tools such as ER/Studio or equivalent is an advantage.

• Experience with data product, Data Mesh, Data Fabric, or domain-oriented data design approaches.

• Understanding of semantic layers used for analytics, reporting, generative AI, and agent-based solutions.

• Working knowledge of SQL and data engineering principles sufficient to validate model implementation and collaborate effectively with engineering teams.

• Experience with enterprise metadata catalogues and business glossary solutions.