The Digital Transformation, Innovation and Data Division helps the organisation make better decisions, work efficiently, and innovate confidently by strengthening data use, developing business intelligence, and enabling responsible AI adoption.
The Director (Enterprise Data) is the organisation's senior authority for enterprise data platforms, data engineering, and data readiness. They are design, govern, and scale the core data infrastructure that ensures data across the whole of A.STAR is trusted, consistent, and ready for analytics and AI. This role sets the long-term direction for the organisation's data architecture, leads the team that delivers and maintains it, and ensures alignment with IT Shared Services, the CDO Office, and enterprise governance forums. A suitable candidate may have at least 10 years of experience in data engineering, enterprise data platforms, analytics, or digital transformation roles.
Key Responsibilities
A. Strategic Leadership
Lead and evolve the Enterprise Data Roadmap, enabling enterprise Business Intelligence, AI, and integration of emerging digital initiatives.
Define the enterprise data architecture and operating model that supports both HQ divisions and 17 Research Institutes.
Provide strategic leadership to Data Engineers, Data Analysts, Data Scientists, ensuring capability development, succession planning, and high-quality delivery.
Shape the organisation's long-term approach to data quality, metadata, and platform sustainability.
B. Data Platform & Engineering Excellence
Oversee the end to end management of the organisation's secure data-landing and ingestion layer.
Lead the design, optimisation, and scalability of the enterprise Data Warehouse for analytics and AI.
Govern our Data Catalogue software as the authoritative metadata, lineage, and stewardship system. Ensuring accountability and discoverability across all datasets.
Set the standards for data modelling, transformation logic, and provisioning of certified data marts for whole of organisation dashboards.
C. Data Quality, Standards & Enablement
Partner with IT Shared Services (Data Governance) to operationalise data governance, ensuring that policies are embedded into day to day data operations.
Establish and maintain standards for data quality, validation, naming conventions, and business definitions.
Collaborate with DTID team members to design data assets that reflect business logic and drive adoption in dashboards and AI use cases.
Enable self-service analytics by ensuring certified datasets are reliable, documented, and easy to access.
D. Partnership & Governance Alignment
Represent DTID in enterprise digital and data forums, including the Joint Digital Policy Forum, ensuring coherence across organisations data practices.
Build strong, sustained relationships with system owners, data stewards, Infrastructure teams, and HQ/RI leaders.
Provide strategic advice to leadership on data risks, emerging technologies, and implications for digital transformation.
E. Performance, Reliability & Value Realisation
Define, track, and report key performance indicators for data pipelines, platform reliability and data quality.
Demonstrate measurable impact of data investments through improvements in BI delivery, AI readiness, and operational efficiency.
Continuously assess platform scalability and cost-efficiency to support long term sustainability.
Must Have
Bachelors or Masters Degree in Computer Science, Data Science, Engineering, Information Systems, or related discipline.
Demonstrated success in designing and operating enterprise-scale platforms using Azure Data Factory, Snowflake, and Alation.
Experience establishing enterprise data governance models, standards, and cross-entity data practices is advantageous.
Qualifications & Experience
Proven leadership experience overseeing multi-tier data teams (Managers through Senior Assistant Directors).
Strong understanding of governance and security frameworks (e.g., IM8, PDPA).
Exceptional communication and stakeholder-management skills, able to influence across complex matrix structures.
Track record in collaborating with BI and AI teams to deliver robust data foundations for downstream products.
