Key Responsibilities:
Data as a Product & Strategic Asset
* Act 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 Leadership
* Partner with backend and platform engineering teams to design and govern:
* Event-driven data flows
* Canonical entity service
* Bi-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 & Observability
* Introduce pragmatic and scalable practices for:
* Data quality monitoring
* Schema evolution and versioning
* Lineage tracking and observability
* Champion explicit data entitlements and purpose-based access controls, ensuring compliance, auditability, and trust are designed into the platform from the outset.
Analytics, AI & Enablement
* Ensure 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 Building
* Act 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 & Background
* Senior 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 & Approach
* Systems-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.
Practicalities
* Willingness 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 Experience
Data 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.