VP, Data Products (Enterprise Data Platforms & Discovery), Innovation Group

Company: 1011 United Overseas Bank Ltd

About UOB

United Overseas Bank Limited (UOB) is a leading bank in ASEAN with a global network in Southeast Asia, Asia Pacific, Europe and North America. Operating through our head office in Singapore and banking subsidiaries in China, Indonesia, Malaysia, Thailand and Vietnam, we have a global network of about 430 branches and offices in 19 markets. At the heart of UOB is our culture, shaped by the UOB Way and anchored on our four values – Honourable, Enterprising, United and Committed. For more than 90 years, these values have guided how we do right by our customers, collaborate with one another and create long-term value for the communities we operate in. As One Bank, we are committed to helping our colleagues build sustainable careers grounded in purpose, supported by strong values, and enriched with meaningful opportunities to grow.

Job Description

Data Office, Innovation Group

Data Office is the Group’s enterprise function responsible for the bank’s enterprise big data analytics platform and for driving data monetization across the bank.

Operating within the Innovations Group, Data Office enables the bank to unlock value from data by delivering making data easily accessible and discoverable, while ensuring regulatory compliance and control are embedded into how data is accessed and used.

At the same time, Data Office is responsible to ensure the enterprise data foundation and platform can evolve with new business demands through identifying new capabilities required and delivering these with our technology partners.

Role Purpose

The VP, Data Products & Innovation will design, build and deliver the the bank’s enterprise Data Products and Data-as-a-Services. The role will help transform priority data assets into trusted, governed, reusable, and business-ready data products that enable analytics, AI, reporting, innovation, and measurable value creation across the Group.

This role will work closely with business units, data engineering, technology, governance, risk, and innovation teams to translate business needs into scalable data product use cases. This role will play a key role in shaping product requirements, coordinating delivery, supporting adoption, and ensuring data products are built with the right controls, documentation, usability, and value tracking.

The successful candidate will be a hands-on data and AI transformation professional with strong product thinking, stakeholder engagement skills, banking domain awareness, and the ability to support enterprise data initiatives from ideation through to operational adoption.

Key Responsibilities

Enterprise Data Products and Data-as-a-Service Strategy

• •Lead the development and execution of the enterprise Data Products and Data-as-a-Service solutions, aligned to business priorities, analytics, AI, digital transformation, and governance requirements.
• •Contribute to the definition of the data product operating model, including product ownership, lifecycle management, prioritization, delivery standards, service support, and value tracking.
• •Work with business units to identify, shape, and priorities data product use cases with clear business objectives, consumption needs, adoption plans, and expected benefits.
• •Promote reusable, governed, and scalable data products as an alternative to one-off data extraction and project-based data delivery.
• •Support the enterprise data enablement shopfront by coordinating data services, advisory support, user guidance, and training activities.

Data Product Delivery, Engineering and Operationalization

• •Lead the delivery, curation, and publication of high-quality datasets and data products for reporting, analytics, AI, and business decisioning.
• •Develop the operationalization of data product applications and self-service consumption channels for business users, ensuring usability, reliability, governance, and access controls are considered.
• •Partner with technology and platform teams to help ensure data products are secure, documented, monitored, and aligned to enterprise architecture and data governance standards.
• •Provide data product support for strategic initiatives and Innovation Challenge use cases, helping teams experiment with data while preparing scalable pathways for enterprise adoption.

Centre of Excellence, Delivery Discipline and Capability Building

• •Support the Data Products Centre of Excellence by providing advisory support, delivery coordination, reusable templates, governance guidance, and adoption frameworks for enterprise data initiatives.
• •Help establish repeatable practices for use case intake, prioritization, discovery, product design, data readiness assessment, operationalization, and benefits tracking.
• •Drive delivery discipline across assigned data product initiatives by managing milestones, dependencies, risks, issues, stakeholder updates, and implementation readiness.
• •Coordinate across data product management, data engineering, governance, service management, technology, and business adoption teams to ensure smooth delivery.
• •Encourage a culture of customer-centricity, reuse, accountability, innovation, risk awareness, and continuous improvement.

Active Metadata, Data Quality & AI Context Enablement

• •Define and maintain enterprise standards for active and passive metadata, business glossary, lineage narratives, data quality rules, and contextual knowledge assets aligned to data products, domains, and business use cases.
• •Establish practical operating models for metadata stewardship, lifecycle management, ownership, adoption tracking, and continuous improvement across data products and enterprise data services.
• •Curate and manage high-quality, governed context, including definitions, policies, controls, lineage explanations, quality rules, and data usage guidance, to support GenAI, RAG-driven solutions, and agentic workflows.
• •Enable reliable retrieval, reasoning, and reuse by structuring enterprise knowledge into AI-consumable artefacts that are source-mapped, versioned, auditable, and aligned to regulatory and risk requirements.
• •Partner with platform, engineering, governance, and business teams to embed metadata, lineage, quality, and context signals into data products, AI solutions, and agent execution paths.
• •Support agent orchestration use cases by helping design metadata and quality services for intent routing, context selection, confidence signalling, explainability, and controlled recommendations.
• •Work with data engineering teams to document data product lineage and define quality dimensions, thresholds, and rules that can be monitored, executed, or recommended by AI-enabled workflows.
• •Translate complex data, platform, governance, and quality concepts into clear knowledge artefacts, playbooks, and reusable guidance that improve adoption across business, analytics, and innovation teams.

Key Skills & Experience

Mandatory Experience

8+ years of experience in enterprise data applications, data transformation, data products, analytics enablement, enterprise platforms, or related leadership roles.

Proven track record leading enterprise-scale data initiatives in banking, financial services, or another complex regulated environment.

Strong experience building or scaling data products, Data-as-a-Service capabilities, self-service data platforms, data discovery services, or enterprise analytics enablement functions.

Demonstrated ability to partner with senior business stakeholders to shape demand, define value cases, prioritise use cases, and drive measurable outcomes.

Strong understanding of data governance, data quality, metadata, lineage, access controls, data risk management, and operational controls.

Proven leadership of cross-functional teams across business, data, technology, governance, and risk domains.

Excellent executive communication skills, with the ability to simplify complex data topics and influence decision-making