Key Responsibilities:Data as a Product & Strategic AssetAct as the primary technical owner of the organisation's data assets, treating them as long-lived, governed products rather than implementation artefacts.Define and evolve canonical data models, ensuring semantic consistency across applications, analytics platforms, and integrations.Establish clear system-of-record principles, data ownership boundaries, and lifecycle management standards.Platform & Architecture LeadershipPartner with backend and platform engineering teams to design and govern:Event-driven data flowsCanonical entity serviceBi-directional synchronisation and conflict-resolution patterns (as the platform evolves)Ensure data architecture decisions support enterprise-scale use cases, not just isolated workflows.Act as a trusted reviewer and decision-maker on data-intensive architectural designs and trade-offs.Data Governance, Quality & ObservabilityIntroduce pragmatic and scalable practices for:Data quality monitoringSchema evolution and versioningLineage tracking and observabilityChampion explicit data entitlements and purpose-based access controls, ensuring compliance, auditability, and trust are designed into the platform from the outset.Analytics, AI & EnablementEnsure analytics and AI initiatives are built on well-defined, reliable, and trustworthy datasets.Collaborate closely with analytics engineers and data scientists to define reusable metrics, features, and datasets.Support leadership in distinguishing between foundational data architecture and downstream insight delivery, avoiding premature optimisation.Culture & Capability BuildingAct as a mentor and multiplier for engineers and analysts, raising overall organisational data maturity.Bring clarity, empathy, and pragmatism when working with teams transitioning from workflow-focused applications to platform-oriented thinking.Serve as a consistent advocate for sound data principles in day-to-day technical decisions, not just strategic discussions.Experience & BackgroundSenior experience in data platform, data architecture, or head-of-data roles within SaaS, platform-based, or data-intensive businesses.Demonstrated experience designing and governing shared data models used across multiple products, domains, or user groups.Hands-on experience spanning operational systems, analytics platforms, and event-driven architectures.Exposure to data governance, access entitlements, or regulated data-sharing environments is highly desirable.Mindset & ApproachSystems-oriented thinker focused on long-term sustainability rather than short-term pipeline delivery.Comfortable balancing architectural best practices with real-world delivery constraints.Able to clearly communicate complex data concepts to engineers, product leaders, and executive stakeholders.Strong focus on data trust - including how data is created, governed, shared, and consumed.AI-literate and pragmatic, able to leverage AI tools where appropriate while maintaining critical judgement.PracticalitiesWillingness to collaborate closely with distributed teams across multiple regions.Comfortable operating within a scaling organisation where processes and structures continue to evolve.Committed to fostering an inclusive and diverse working environment.Technology Landscape (Indicative, Not Prescriptive)Specific technologies may evolve, the role will operate across areas such as:Operational Datastores (e.g. PostgreSQL, MySQL, cloud-managed relational databases)Event & Data Movement Patterns (e.g. Kafka, Pub/Sub, cloud-native messaging systems)Analytics & Data Platforms (e.g. BigQuery, Snowflake, Redshift, modern lakehouse architectures)Schema, Contracts & Versioning (e.g. schema registries, data contracts, API-first design)Desired Skills and ExperienceData as a Product & Strategic Asset: Own and steward data as a long-lived, governed product; define and evolve canonical data models to ensure semantic consistency; establish clear system-of-record principles, ownership boundaries, and lifecycle management standards.Platform & Architecture Leadership: Partner with engineering teams to design and govern event-driven data flows, canonical entity services, and synchronisation patterns; ensure architecture supports enterprise-scale use cases; act as a senior reviewer and decision-maker on data-centric design trade-offs.Data Governance, Quality & Observability: Implement scalable practices for data quality monitoring, schema versioning, lineage, and observability; design and enforce entitlement and purpose-based access frameworks to ensure compliance, auditability, and data trust.Analytics, AI & Enablement: Provide strong data foundations for analytics and AI initiatives; collaborate with analytics engineers and data scientists to define reusable datasets, metrics, and features; guide leadership on separating foundational platform work from downstream insight delivery.Culture & Capability Building: Mentor engineers and analysts to elevate data maturity; embed platform-oriented thinking across teams; act as a consistent advocate for sound data principles in day-to-day technical and architectural decisions.