Role Purpose
The Data Architect is responsible for shaping and driving the company Investments’ data strategy through the design, oversight, and enhancement of the enterprise data architecture. This role ensures that all data initiatives, platforms, and models align with the Group’s overarching data framework and strategic direction.
A key focus of the role is translating business strategy into practical, scalable data capabilities. The Data Architect influences and educates stakeholders on data platforms, best-practice usage, and the value of data-driven decision-making.
The role operates as a strategic advisor to business and technology leadership, influencing enterprise data and AI investment decisions, platform modernisation, and the adoption of data-centric operating models. The position oversees the implementation and monitoring of data-related work and projects to ensure alignment with architectural standards, governance practices, and long-term data capability maturity.
Key Performance Areas
Role Outputs
Perspective: Strategy & Process
- Lead the development, evolution, and socialisation of the organisation’s data and AI architecture in alignment with the Group’s data strategy and enterprise data framework
- Own and drive the enterprise-wide data architecture roadmap across multiple business domains and platforms
- Provide strategic input on the evolution of data and AI capabilities, platforms, and processes to support business strategy
- Translate strategic objectives into tactical implementation plans for data and AI projects and platform enhancements
- Design, refine, and govern enterprise data and AI models, flows, and integration patterns to ensure consistency, scalability, and interoperability
- Define target-state architecture and ensure alignment of all initiatives to enterprise architecture principles
- Monitor and evaluate the execution of data and AI initiatives to ensure adherence to standards, architectural principles, and expected business value
- Establish and maintain data and AI architecture standards, reference architectures, design patterns, and supporting documentation
- Identify opportunities to optimise data availability, data quality, and data utilisation across the organisation
- Provide architectural oversight across multiple concurrent strategic programmes and initiatives
Perspective: Finance
- Ensure strategic data and AI decisions balance long-term sustainability with cost-effectiveness
- Influence and guide data platform and AI investment decisions (including cloud, tooling, and advanced analytics capabilities)
- Evaluate data platform and AI investments to ensure alignment with architectural direction and optimal return on investment
- Support financial and business analytics functions through accurate, well-governed data structures and processes
- Track and optimise costs associated with storage, compute, and data movement across environments
- Identify opportunities for data value realisation and monetisation through advanced analytics and AI
Perspective: Client Services / Stakeholder Engagement
- Influence, guide, and educate business stakeholders on data platform capabilities, AI opportunities, appropriate usage, and data-driven decision-making
- Serve as a key advisor on data and AI implications of business initiatives, ensuring alignment with architectural principles and governance
- Act as a trusted advisor to senior and executive leadership on data and AI strategy and related investment decisions
- Promote and embed a culture of responsible data usage and ownership across the organisation
- Translate complex data and AI architecture concepts into clear, accessible business language
- Provide consultation and training to uplift data and AI literacy and capability across teams
Perspective: People
- Foster a collaborative, cross-functional environment with shared accountability for data assets
- Mentor and support team members involved in data and AI engineering, analytics, and data management
- Promote continuous learning and development related to modern data and AI practices, tools, and frameworks
- Encourage knowledge sharing to uplift organisational data and AI capability maturity
- Provide technical leadership and thought leadership across data communities within the organisation
Qualifications & experience
- Bachelor’s degree in Computer Science, Information Technology, or a related field
- Advantageous: Certifications in data architecture, data management, cloud platforms, data analytics, machine learning, applied AI, or enterprise architecture
- 8–12+ years’ experience in data architecture, data modelling, data analytics, machine learning, applied AI, or enterprise data management
- Proven experience in leading or shaping enterprise-scale data architecture and transformation initiatives
- Deep understanding and proven success in building data frameworks, embedding data governance, managing metadata, and creating and socialising master data principles
- Strong proficiency in database technologies, data integration patterns, and modern data platforms (cloud and on-premise)
- Proven experience with Azure, AWS, or Google Cloud data services
- Experience across multi-domain or complex data environments, ideally within financial services or similarly regulated industries
- Proven experience in influencing stakeholders and driving the adoption of data practices
- Demonstrated experience in strategic and technical AI/analytics leadership, AI-driven opportunity identification, and shaping AI and analytics capability roadmaps
Competency
- Demonstrates strong analytical, conceptual, and data-modelling capabilities
- Applies strategic thinking to translate business goals into clear architectural direction
- Exhibits excellent communication, stakeholder engagement, and influencing skills
- Shows adaptability and resilience within dynamic, rapidly evolving environments
- Possesses solid project management expertise with the ability to coordinate and oversee multiple initiatives simultaneously
- Displays a strong passion for data and AI enablement, education, and capability uplift across the organisation
- Holds sound knowledge of AI and machine learning architecture
- Able to effectively translate AI concepts into tangible business value
- Experienced in integrating AI solutions into enterprise data architectures
- Collaborates effectively with data scientists and data engineers to enable AI-driven delivery
- Strong commercial awareness and ability to balance innovation with cost and value outcomes
