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

About UOB

United Overseas Bank Limited (UOB) is a leading bank in Asia with a global network of more than 500 branches and offices in 19 countries and territories in Asia Pacific, Europe and North America. In Asia, we operate through our head office in Singapore and banking subsidiaries in China, Indonesia, Malaysia and Thailand, as well as branches and offices. Our history spans more than 80 years. Over this time, we have been guided by our values – Honorable, Enterprising, United and Committed. This means we always strive to do what is right, build for the future, work as one team and pursue long-term success. It is how we work, consistently, be it towards the company, our colleagues or our customers.

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 at senior levels. 

Preferred Experience 

• Experience establishing a Data Products organization, Data Product CoE, data marketplace, enterprise data catalogue, data discovery platform, or governed self-service data capability. 

• Exposure to AI, advanced analytics, machine learning, customer analytics, risk analytics, regulatory reporting, or digital banking use cases. 

• Experience with modern data ecosystems, including cloud data platforms, data lakes, data warehouses, data catalogues, API-based data services, and analytics tools. 

• Familiarity with product management practices, agile delivery, service management, change adoption, and benefits realization frameworks. 

Leadership Attributes 

• Transformation-oriented: Able to move the organization from fragmented data delivery to scalable, product-led enterprise data consumption. 

• Business value focused: Relentless about linking data initiatives to business outcomes, adoption, and measurable enterprise benefits. 

• Risk-aware and pragmatic: Balances speed, usability, governance, and control in a regulated banking environment. 

• Influential communicator: Builds trust with executives, business users, engineers, governance teams, and risk partners. 

• Builder mindset: Comfortable designing operating models, forming teams, shaping roadmaps, and creating repeatable enterprise capabilities. 

Additional Requirements

Be a Part of the UOB Family

UOB is an equal opportunity employer. UOB does not discriminate on the basis of a candidate's age, race, gender, color, religion, sexual orientation, physical or mental disability, or other non-merit factors. All employment decisions at UOB are based on business needs, job requirements and qualifications. If you require any assistance or accommodations to be made for the recruitment process, please inform us when you submit your online application.

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