VP/AVP, Specialist, Marketing Data Product Owner, Group Marketing & Communications

Business Function:

Group Marketing & Communications is responsible for building, protecting, and enhancing the DBS brand in an increasingly commoditised banking industry. Working closely with all units within the DBS Group, we create and execute integrated strategies that focus on communications especially digital communications, marketing, and research to differentiate - and elevate - our brand from the rest of the competition.

About the Role

The Marketing Data Product Owner will define, build and scale the data capabilities required to support marketing across audience management, campaign activation, performance analytics and measurement. The role owns the data product roadmap and delivery of reusable data products that enable marketers to identify and activate relevant audiences, measure outcomes and make better investment decisions.

Working at the intersection of Marketing, Data and Technology, the role will translate business needs into clear product requirements, lead delivery with engineering and analytics teams, validate solutions through testing, and drive adoption and continuous improvement across teams and markets.

Key Responsibilities

• Own and deliver the data products for marketing transformation, manage delivery of enterprise marketing knowledge base
• Own the data product roadmap and prioritized backlog for marketing data capabilities, balancing business value, reuse, feasibility and delivery dependencies.
• Define reusable audience data products, including segment logic, audience composition rules, data schemas and the enterprise Audience Segment Library.
• Develop prospect and new-to-bank audience capabilities and convert high-value historical campaign audiences into reusable segments for future activation.
• Define marketing measurement products, including self-service performance dashboards, common metrics, attribution and Marketing Mix Modelling capabilities.
• Translate marketing use cases into clear product requirements, user stories, functional and data specifications, acceptance criteria and non-functional requirements.
• Partner with Data Engineering, Analytics, MarTech platform and Technology teams throughout solution design and delivery, resolving scope, sequencing and implementation trade-offs.
• Lead business validation, user acceptance testing and release readiness to ensure delivered capabilities meet agreed use cases, data quality expectations and performance criteria before go-live.
• Drive product launch, documentation, adoption and continuous improvement by monitoring usage, reliability, user feedback and measurable business outcomes.

Requirements

• 8–12 years of experience in data product management, customer data, marketing analytics, CRM, segmentation, MarTech or a related field.
• Demonstrated ownership of data products from product strategy and discovery through requirements, delivery, launch, adoption and continuous improvement.
• Experience defining audience, customer or prospect data products, including segment logic, audience composition, taxonomy, lifecycle and activation requirements.
• Proven ability to translate business needs into user stories, data requirements, schemas, data contracts, acceptance criteria and engineering-ready specifications.
• Practical understanding of marketing measurement, paid media performance reporting, attribution and Marketing Mix Modelling.
• Strong SQL and data fluency, with the ability to understand data models, identity resolution, data quality, lineage and batch or real-time integration patterns.
• Experience working with customer data platforms, audience platforms, cloud data platforms, data warehouses, APIs and business intelligence tools such as Power BI or Tableau.
• Experience leading user acceptance testing, validating data products against business scenarios and managing release readiness with engineering and business teams.
• Strong product judgement, stakeholder management and executive communication skills, with the ability to align Marketing, Data and Technology teams and make clear prioritisation trade-offs.

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