Editorial summary

The Deputy Manager (Data Hub & Data Products) at the Office of Digitalisation, Data and AI supports the design, development, and operation of an enterprise Data Hub for an Institute of Higher Learning. The role requires expertise in data architecture, data modeling, and data product management to ensure the delivery of trusted data assets that facilitate analytics, reporting, and AI initiatives. The incumbent collaborates with internal stakeholders and external vendors to translate business needs into scalable data solutions. Compensation details are not specified.

This summary is AI-generated and may contain inaccuracies. Please refer to the full job description below.

Job description

[This job id 32416 first appeared in Job-Q.com on 16 Sep 2026]

[What the role is]

The Deputy Manager, Data Hub & Data Products supports the design, development, and operationalisation of an Institute of Higher Learning (IHL) enterprise Data Hub.

The role combines capabilities in data architecture, data modelling, and data product management to ensure data assets are organised, governed and delivered as trusted data products that support analytics, reporting, AI and decision-making.

The incumbent will work closely with Schools, Departments, business stakeholders, and external technology vendors to translate business requirements into scalable enterprise data models and data products.

[What you will be working on]

1. Data Product Management
•    Support the development and delivery of enterprise data products aligned with IHL's strategic priorities.
•    Work with business stakeholders to understand business needs, data requirements and desired outcomes.
•    Translate business use cases into data product requirements, user stories and delivery roadmaps.
•    Prioritise data product improvements based on business value and institutional impact.
•    Drive adoption and usage of Data Hub data products across the institute.
•    Monitor data product performance, usage and business outcomes.
•    Support the implementation of Data-as-a-Service (DaaS) capabilities.

2. Enterprise Data Modelling
•    Design and maintain conceptual, logical and physical data models for enterprise-wide data domains.
•    Develop scalable data models that support analytics, operational reporting and AI use cases.
•    Define and maintain business entities, relationships, business rules and metadata.
•    Ensure consistency and alignment of data definitions across systems and domains.
•    Support the development of enterprise semantic models and reusable datasets.
•    Collaborate with Data Owners and Data Stewards to establish trusted data definitions.

3. Data Architecture and Integration
•    Support the Lead Data Architect in designing enterprise data architecture and target-state Data Hub architecture.
•    Review solution designs proposed by vendors and ensure alignment with enterprise architecture principles and standards.
•    Support data ingestion, transformation, integration and data lifecycle design activities.
•    Assess scalability, performance, maintainability and usability of architecture solutions.
•    Contribute to enterprise data architecture blueprints, standards and reference architectures.
•    Ensure effective integration across enterprise applications, learning systems and analytics platforms.

4. Data Warehouse and Data Hub Delivery
•    Lead technical discussions relating to design and implementation.
•    Review ETL/ELT pipelines, data transformation logic and data quality processes.
•    Support onboarding of new source systems into the Data Hub.
•    Identify data quality, performance and integration issues and coordinate resolution efforts.
•    Support testing, validation and release activities for Data Hub initiatives.
•    Ensure data assets are AI-ready, reusable and scalable.

5. Vendor and Technical Management
•    Provide day-to-day oversight of external vendors delivering Data Hub solutions.
•    Review vendor deliverables, technical documentation and implementation approaches.
•    Challenge proposed designs where necessary and ensure solutions meet business requirements.
•    Perform root cause analysis of data-related issues and coordinate resolution.
•    Be hands-on in investigating data pipeline failures, integration issues, data discrepancies and performance bottlenecks.
•    Escalate delivery, architecture or quality issues when required.
•    Ensure knowledge transfer and documentation are completed by vendors.

[What we are looking for]

•    Qualification in Information Systems, Computer Science, Data Analytics, Data Engineering or related discipline.

Experience
•    At least 5 years of experience in:
o    Data Warehousing
o    Enterprise Data Management
o    Data Architecture
o    Data Modelling
o    Data Engineering
o    Business Intelligence or Analytics Platforms
•    Experience delivering enterprise-scale data platforms.
•    Experience managing vendors and technology implementation projects.
•    Experience working in education, public sector or large complex organisations is advantageous.
•    Familiarity with IHL business processes and data domains is highly desirable.

Knowledge & Skills
•    Strong knowledge of: 
o    Data Warehousing concepts
o    Dimensional Modelling
o    Star and Snowflake Schemas
o    Modern data architecture principles
o    ETL / ELT design
o    Master and Reference Data Management
o    Metadata Management
o    Data Quality Management
o    Microsoft Fabric, Azure Data Services
•    Ability to communicate effectively with both technical and non-technical stakeholders.
•    Strong facilitation, negotiation and influencing skills.
•    Ability to bridge business, technology and data teams.

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