Job description

Enterprise data scattered across operational systems, analytics platforms and AI initiatives needs one architecture to hold it together, and that is what this contract is built around. You define the vision, principles, standards and roadmap for the wider data estate, then translate that into conceptual, logical and physical data models spanning operational, analytical and AI environments. Integration patterns come next, covering batch, streaming, API and event-driven approaches built to scale. Metadata, lineage, master data, data quality, retention and lifecycle management all sit under the same set of architecture standards you develop. Federated ownership matters here too, so domains keep control of their own data while still working inside a common framework that supports business self-service. Platforms including Azure, Microsoft Fabric, Databricks, Snowflake and lakehouse architectures get evaluated and modernised as part of the same remit. Cybersecurity, enterprise architecture, technology and business teams all rely on this role to keep security, privacy and compliance embedded in every data solution. Architecture reviews, technical direction and reusable patterns for data engineering teams round out the day to day work, alongside a constant eye for where enterprise data flows can be simplified or rationalised.