Senior Manager - Product Management (Enterprise Data Foundation)

About the job Established in 2004, Dubai Holding is a global investment company with investments in more than 34 countries and a combined workforce of nearly 45,000 individuals. In line with the vision of Dubai’s leadership and economic diversification strategy, Dubai Holding companies have nurtured sectors, irrevocably transforming Dubai’s socio-economic landscape and positioning Dubai as a diversified, globally integrated economy. Dubai Holding is committed to the diversification of Dubai’s non-oil economy. Our portfolio, valued at over AED 284+ billion, spans 10 sectors, including real estate, hospitality, leisure & entertainment, ICT, design, education, media, retail, manufacturing & logistics, and science. For the Good of Tomorrow Dubai Holding is looking to hire a Senior Manager - Product Management (Enterprise Data Foundation) at the corporate office, reporting to the Director – AI Product Lead. The role holder is responsible for building and strengthening the Group’s data and knowledge foundation, integrating existing data estates including Foundry and Oracle, and onboarding new internal and external sources. The role holder is accountable for converting fragmented data into curated, governed and reusable knowledge assets that can be consumed consistently by AI agents and analytics products across the Group, working alongside other Product Managers within the portfolio strategy and technology priorities set by the Director - AI Product Lead. The role holder takes a product-led approach to data, treating datasets, semantic models and knowledge assets as products with defined owners, consumers, quality standards and lifecycles. This requires close partnership with data engineering, architecture, governance and business data owners to ensure the foundation is trusted, compliant and fit for agentic consumption. As data dependencies sit on the critical path for AI product delivery, the role holder is responsible for ensuring knowledge assets are available, accurate and fit for purpose ahead of product need. Key Responsibilities: Data and Knowledge Product Strategy • Define and own the roadmap for the Group’s data and knowledge foundation, ensuring alignment with AI product priorities, enterprise data strategy and the direction of the AI product portfolio. • Prioritize domains and sources based on the value they unlock for AI products and business decision-making. • Establish the target architecture for reusable knowledge assets, including semantic models, ontologies and retrieval-ready data stores. • Take decisions within delegated authority on data design, prioritization and asset standards, escalating matters affecting portfolio direction, enterprise architecture or investment envelopes. Data Integration and Source Onboarding • Integrate existing databases and platforms, including Foundry and Oracle, into a coherent and accessible foundation. • Onboard new internal and external data sources through a structured, repeatable intake and integration process. • Resolve duplication, inconsistency and fragmentation across overlapping data estates. • Manage data dependencies that sit on the critical path for AI product delivery, ensuring sources are available and fit for purpose ahead of product need. Reusable Knowledge Assets and Innovation • Design and deliver curated and documented knowledge assets that multiple AI products and agents can consume without rework. • Define standards for metadata, lineage, versioning and discoverability so assets can be found and trusted. • Drive measurable reuse of knowledge assets across the product portfolio rather than per-use-case data builds. • Maintain awareness of emerging practice in knowledge engineering, retrieval, semantic modelling and data products, and evolve the foundation accordingly. • Run structured experimentation to validate new approaches before committing them to the roadmap. Performance Management and Value Realization • Define, track and manage KPIs covering data coverage, quality, freshness, reuse and downstream product enablement. • Monitor the impact of foundation improvements on AI product performance and adoption. • Identify risks, dependencies and performance gaps, implementing mitigation and improvement actions. • Build and challenge business cases and investment proposals for data foundation initiatives. Data Governance and Compliance • Embed data quality controls, ownership and stewardship into the foundation in line with the Group Data Governance Policy. • Ensure data classification, retention, residency and privacy requirements are met for all onboarded sources. • Work with IT GRC and Data Protection and AI Legal to ensure data use in AI products is lawful, documented and auditable. • Provide the evidence and documentation required by governance, risk and assurance functions. Stakeholder Management and Cross-Functional Delivery • Act as the primary interface between business data owners, governance forums and AI product teams. • Collaborate with data engineering, platform, architecture and cybersecurity stakeholders to prioritise and execute foundation initiatives. • Ensure alignment between technical feasibility, business objectives and delivery timelines across squads. • Negotiate access, ownership and stewardship arrangements across verticals with competing interests, escalating and resolving cross-team dependencies and resourcing conflicts Who we are looking for: • 6 – 8 years of experience in data management, data engineering or data/platform product management roles within large or complex organizations. • Demonstrated experience integrating heterogeneous enterprise data estates and delivering reusable data or knowledge assets. • Experience enabling AI, analytics or machine learning consumption of enterprise data. • Experience operating within a formal data governance framework is advantageous. Education / Professional Certifications: • Bachelor’s degree in a quantitative field such as computer science, engineering, information technology or mathematics. • Data management or cloud data platform certification, such as DAMA CDMP or Azure/AWS data credentials, is advantageous. Technical Competencies: • Experience applying product management practice, including discovery, prioritisation and lifecycle management, to data or platform products. • Strong working knowledge of enterprise data platforms and databases, including Foundry, Oracle and cloud data warehouses. • Experience with data modelling, semantic layers, ontologies and knowledge graph or vector-based retrieval approaches. • Understanding of data pipelines, integration patterns, APIs and streaming versus batch trade-offs. • Practical grasp of data quality, lineage, metadata and master data management disciplines. • Familiarity with data classification, privacy and residency requirements under applicable regulation. • Ability to translate business requirements into scalable, reusable data and knowledge assets. • Familiarity with Group AI, data protection and information security requirement

6 – 8 years of experience in data management, data engineering or data/platform product management roles within large or complex organizations. Demonstrated experience integrating heterogeneous enterprise data estates and delivering reusable data or knowledge assets. Experience enabling AI, analytics or machine learning consumption of enterprise data. Experience operating within a formal data governance framework is advantageous. Bachelor’s degree in a quantitative field such as computer science, engineering, information technology or mathematics. Data management or cloud data platform certification, such as DAMA CDMP or Azure/AWS data credentials, is advantageous.

Define and own the roadmap for the Group’s data and knowledge foundation, ensuring alignment with AI product priorities, enterprise data strategy and the direction of the AI product portfolio. Prioritize domains and sources based on the value they unlock for AI products and business decision-making. Establish the target architecture for reusable knowledge assets, including semantic models, ontologies and retrieval-ready data stores. Take decisions within delegated authority on data design, prioritization and asset standards, escalating matters affecting portfolio direction, enterprise architecture or investment envelopes. Integrate existing databases and platforms, including Foundry and Oracle, into a coherent and accessible foundation. Onboard new internal and external data sources through a structured, repeatable intake and integration process. Resolve duplication, inconsistency and fragmentation across overlapping data estates. Manage data dependencies that sit on the critical path for AI product delivery, ensuring sources are available and fit for purpose ahead of product need. Design and deliver curated and documented knowledge assets that multiple AI products and agents can consume without rework. Define standards for metadata, lineage, versioning and discoverability so assets can be found and trusted. Drive measurable reuse of knowledge assets across the product portfolio rather than per-use-case data builds. Maintain awareness of emerging practice in knowledge engineering, retrieval, semantic modelling and data products, and evolve the foundation accordingly. Run structured experimentation to validate new approaches before committing them to the roadmap. Define, track and manage KPIs covering data coverage, quality, freshness, reuse and downstream product enablement. Monitor the impact of foundation improvements on AI product performance and adoption. Identify risks, dependencies and performance gaps, implementing mitigation and improvement actions. Build and challenge business cases and investment proposals for data foundation initiatives. Embed data quality controls, ownership and stewardship into the foundation in line with the Group Data Governance Policy. Ensure data classification, retention, residency and privacy requirements are met for all onboarded sources. Work with IT GRC and Data Protection and AI Legal to ensure data use in AI products is lawful, documented and auditable. Provide the evidence and documentation required by governance, risk and assurance functions. Act as the primary interface between business data owners, governance forums and AI product teams. Collaborate with data engineering, platform, architecture and cybersecurity stakeholders to prioritise and execute foundation initiatives. Ensure alignment between technical feasibility, business objectives and delivery timelines across squads. Negotiate access, ownership and stewardship arrangements across verticals with competing interests, escalating and resolving cross-team dependencies and resourcing conflicts.