The Opportunity 

Lead the Data Modernisation Squad responsible for building, modernising and continuously improving the enterprise data engineering ecosystem.

The role owns the roadmap, backlog and adoption of reusable engineering capabilities including the ADAPT platform, Azure, UWF, Teradata modernisation, batch and integration frameworks, AI agents, AMP, GitHub Copilot (GHC), automation tooling and self-service engineering capabilities.

The Product Owner works closely with engineers to simplify how data products and pipelines are designed, built, tested, deployed and supported across the enterprise.

Role Focus

• Own the vision, roadmap and backlog for Data Delivery Modernisation.
• Lead delivery of reusable frameworks, standards, templates and engineering accelerators.
• Drive adoption of ADAPT, Azure-based engineering frameworks and enterprise integration capabilities.
• Support migration and modernisation initiatives from legacy technologies including Teradata and legacy batch platforms.
• Champion AI-assisted engineering through AMP, GitHub Copilot and engineering agents.
• Continuously improve the engineering experience by reducing complexity and manual effort.
• Ensure engineers have the tools, frameworks and patterns required to deliver consistently and efficiently.

Key Accountabilities

• Drive adoption and continuous improvement of the ADAPT platform.
• Lead development and rollout of Azure UWF engineering frameworks and reusable patterns.
• Support Teradata simplification, migration and modernisation initiatives.
• Promote consistent engineering standards across enterprise platforms.
• Define and maintain reusable data engineering frameworks.
• Drive standardised pipeline patterns, templates and development accelerators.
• Improve batch processing and integration frameworks across the enterprise.
• Increase self-service capabilities available to engineering teams.
• Reduce manual engineering effort through automation and reusable components.
• Drive adoption of AMP, GitHub Copilot and AI-enabled engineering tools.
• Identify opportunities to leverage AI agents for pipeline generation, testing, documentation and engineering support.
• Improve engineering productivity through intelligent automation and modern tooling.
• Ensure AI capabilities deliver measurable outcomes for engineering teams.
• Manage the modernisation backlog and prioritise initiatives based on business and engineering value.
• Partner with engineers, architects and delivery teams to understand needs and pain points.
• Define success measures and track adoption across engineering capabilities.
• Ensure capability investments deliver tangible productivity and quality improvements.
• Gather feedback from engineering teams and maintain an improvement roadmap.
• Measure usage, adoption and benefits of engineering capabilities.
• Drive simplification and removal of unnecessary complexity.
• Promote a culture of innovation, standardisation and continuous improvement.

Essential Skills & Experience

• Proven experience as a Product Manager or Product Owner leading complex data platform, data modernisation, cloud data, analytics, or AI-enabled products within large-scale enterprise environments.
• Demonstrated success defining and executing product strategies that transform legacy data ecosystems into modern, scalable, secure, and reusable data platforms.
• Strong experience managing product backlogs, prioritisation frameworks, roadmaps, and squad outcomes across Agile and Scrum delivery teams, ensuring alignment to business value and strategic objectives.
• Deep understanding of modern data capabilities including data engineering, cloud data platforms, data governance, analytics, reporting, artificial intelligence, and machine learning enablement.
• Proven ability to work across a complex stakeholder landscape, partnering with business leaders, technology teams, architects, risk, compliance, security, and data governance functions to drive successful outcomes.
• Strong commercial and analytical mindset with the ability to make evidence-based product decisions, balancing customer needs, regulatory requirements, technical constraints, and organisational priorities.
• Experience translating strategic outcomes into epics, features, user stories, acceptance criteria, and measurable product success metrics.
• Strong understanding of data privacy, risk management, security, and regulatory considerations relevant to banking and financial services environments.
• Exceptional stakeholder engagement, communication, and influencing skills, with the ability to build alignment across senior executives, delivery teams, and technical specialists.
• Demonstrated capability leading cross-functional teams through ambiguity, driving product adoption, organisational change, and continuous improvement.
• Strong knowledge of Agile, Scrum, and product-led delivery practices, with a focus on iterative value delivery and customer-centric outcomes.