Data Modelling Lead

The role in context: We are establishing a dedicated Data Modelling team inside GSC Data Analytics to give our enterprise data a designed, governed structure-and to remove the single biggest constraint on our AI ambitions: data that is disconnected, inconsistently mastered and misaligned across systems.

This team is the connective tissue of the Data Backbone.

In our strategy the Data Backbone is the bridge between today's federated landscape and the clean, converged foundation that T. U. K (Titan Unify KunPeng) will deliver over the coming years.

The bridge is what lets us run AI and autonomous planning now-by joining, organising and streamlining data across systems as they exist today, rather than waiting for convergence to finish.

That landscape is heterogeneous-SAP Oracle ERPs, alongside best-of-breed platforms such as Kinaxis (planning) and Manhattan (logistics execution), procurement systems such as Prism and Coupa, and external data sources such as Z2. Bridging these-not just ERP-to-ERP-is the substance of the role.

The team is led by a Director, with three Data Modelling Leads owning distinct domain clusters. This role is one of those three Leads, accountable for the [Procurement/Planning Logistics/Manufacturing] domain.

You own how [Procurement/Planning Logistics/Manufacturing] data connects across systems. You understand how data in one system relates to data in another-despite non-standard master data, coverage gaps and structural differences-and you design the models and mappings that make it connectable, trustworthy and reusable. That mapping is held as an enterprise asset. The boundary that defines this team: This team designs and owns the map-the logical and canonical models, the source-to-target mappings and the definitions-while GSC Data Engineering builds the pipelines and physical structures from those designs. You stay deep in the modelling craft, hands-on with real source data, rather than spread thin across pipeline build. Where your work sits You own, hands-on: conceptual and logical data models, canonical entity and KPI definitions, source-to-target mapping specifications, business-rule and quality-rule definitions, lineage and metadata documentation. You hand off and stay close: well-specified designs go to Data Engineering (including its Technical Data Architects) to build-and you review the build back against the model, staying involved in how it is realised. Your ownership of the map carries real influence over what gets built and what gets cleaned.