Description of role and key responsibilities

The Data Domain Lead is accountable for shaping and embedding the end-to-end data lifecycle for assigned business data domains, from data discovery, capture and sourcing through modelling, integration, storage, governance, analytics, consumption, retention and controlled disposal.

The role combines the core skills of a Data Architect with strong data analytics and data strategy capability. It ensures domain data is designed as a strategic asset, with clear ownership, fit-for-purpose architecture, trusted data products, measurable quality, secure access and reusable patterns that support business decision-making, operational resilience and regulatory obligations.

Working closely with business owners, distributed domain teams, the UK and SA Data teams, Group Engineering, Security, Infrastructure, Governance, Platform teams and the Analytics Enablement Lead, the Data Domain Lead translates business objectives into practical data strategies and architecture roadmaps. The role aligns domain initiatives with enterprise standards, feeds learning and best practice back into the centre of excellence, and helps ensure analytics and AI use cases are delivered on a robust, governed and scalable data foundation.

Key Responsibilities

• Define and maintain the data domain strategy, architecture roadmap and target-state data capabilities, aligned to UK Data and Analytics strategy, business priorities and enterprise architecture standards.

• Own the end-to-end data lifecycle for the domain, ensuring clear processes and controls for data sourcing, ingestion, modelling, integration, transformation, storage, semantic layers, consumption, archival and disposal.

• Develop and govern conceptual, logical and physical data models, domain data products, data contracts, canonical definitions and reusable patterns that enable consistent use of data across operational, analytical and AI use cases.

• Partner with business data owners, SMEs and technology teams to identify data needs, prioritise domain initiatives, define value cases and translate business outcomes into data requirements, architecture decisions and delivery plans.

• Lead data management initiatives across the domain to improve data quality, integrity, metadata, lineage, master/reference data, interoperability, security and compliance with governance policies.

• Ensure analytics outcomes remain central to the domain roadmap by enabling trusted reporting, self-service analytics, Power BI/Microsoft Fabric and Omniscope use cases, and well-designed metrics and semantic models.

• Provide architectural assurance across domain change, including solution design review, data flow assessment, privacy/security considerations, integration patterns, platform alignment and non-functional requirements.

• Collaborate with the platform, governance and analytics enablement teams to adopt best practice, contribute domain learnings back to the centre of excellence and promote consistent standards across distributed data teams.

• Establish and monitor domain data health measures, including data quality KPIs, adoption, lineage completeness, issue resolution, risk indicators and value delivery against strategic objectives.

• Provide leadership and coaching to data and analytics teams, fostering a culture of ownership, architectural discipline, innovation, continuous improvement and evidence-based decision-making.

• Stay current with industry trends and best practice in data architecture, data governance, analytics, cloud data platforms, AI-ready data foundations and regulatory expectations.

• Communicate effectively with senior stakeholders and delivery teams, explaining complex data architecture and analytics concepts in clear business terms to secure alignment and action.

Core skills and knowledge

• Core data architecture capability, including domain modelling, data product design, conceptual/logical/physical modelling, data integration patterns, metadata, lineage, master/reference data and lifecycle management.

• Strong understanding of end-to-end data ecosystems, including source systems, ingestion, transformation, storage, semantic layers, BI consumption, operational data use, AI/ML enablement, retention and disposal.

• Strategic thinker with the ability to translate business objectives into domain data strategies, architecture roadmaps, prioritised backlogs and measurable outcomes.

• Strong data analytics background, with the ability to shape trusted metrics, reporting layers, self-service analytics and insight products that support strategic and operational decision-making.

• Experience with Power BI/Microsoft Fabric and Omniscope essential; able to connect analytics requirements to the underlying data architecture and governance model.

• Strong knowledge of data governance, data quality management, data ownership, security, privacy, access control and regulatory expectations in a financial services environment.

• Ability to assess current-state data landscapes, identify architectural gaps and drive pragmatic improvements across people, process, technology and controls.

• Excellent stakeholder engagement and communication skills, capable of articulating complex data architecture, analytics strategies and trade-offs to both technical and non-technical audiences.

• Detail-oriented with a strong focus on data accuracy, quality, definitions, controls and sustainable design.

• Innovative mindset with a passion for using data, analytics and AI-ready foundations to create business value while maintaining appropriate governance and risk management.

• Ability to work effectively in a fast-paced, federated and dynamic environment, influencing teams without relying solely on formal authority.

Any other attributes that would be helpful, but not essential for the role.

• Experience operating in a federated data organisation or data mesh environment.

• Familiarity with DAMA, TOGAF, Zachman or similar data/enterprise architecture frameworks.

• Experience defining data products, data contracts, semantic models or domain-level data catalogues.

• Experience supporting AI, advanced analytics or model-risk-aware data foundations.

• Financial services, wealth management or regulated industry experience.