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)
