Data Modelling Lead - Multiple Teams

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.

WHAT THIS ROLE IS (AND IS NOT)

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.

These are designed as genuine partnerships, not tickets into a void: Data Engineering builds to your designs and reviews with you, and the GSC Data Office owns fixing the source-data issues you surface. Your ownership of the map carries real influence over what gets built and what gets cleaned.

KEY RESPONSIBILITIES

Enterprise Mapping & Canonical Modelling  (the core of the role)

• Design conceptual and logical data models for the [Procurement/Planning & Logistics/Manufacturing] domain that reflect how the business actually operates.

• Produce source-to-target mappings across ERPs, best-of-breed platforms (e.g. Kinaxis, Manhattan, procurement systems) and external data sources (e.g. Z2), explicitly accounting for non-standard master data, coverage gaps and structural inconsistencies.

• Define canonical entities, business rules & KPI definitions so the same concept means the same thing across squads.

Own the [Procurement/Planning & Logistics/Manufacturing] Map as an Enterprise Asset

• Maintain the mapping and models as a governed, versioned, reusable asset — not a one-off deliverable.

• Keep documentation, lineage and decision rationale transparent and traceable, so the map is trusted and auditable.

Delivering to Demand, Within Projects

• Extend the enterprise map incrementally — object by object, area by area — predominantly in response to demand (AI use cases and business projects), rather than as a single big-bang build.

• Deliver your modelling within project constructs and squads to time-bound commitments, managing dependencies and priorities so modelling keeps pace with delivery.

• Balance this demand-driven delivery with the proactive, foundational modelling that no single project triggers — the work that stands up the backbone itself.

Business Requirements, Backbone Coverage & Data Quality (with the GSC Data Office)

• Translate [Procurement/Planning & Logistics/Manufacturing] business requirements into the data needed to meet them — the essential link between what the business asks for and what the backbone must hold.

• Determine whether that data already exists in the Data Backbone; where it does not, partner with the GSC Data Office to determine the authoritative data source and set it up. (The Data Office owns authoritative-source identification; this role connects it to the business need.)

• Surface and diagnose join-level data-quality issues — where and why data fails to connect across systems — so the Data Office and domains can remediate the underlying transactional source issues.

Design–Build Handoff with Data Engineering  (clear RACI)

• Translate your models into build-ready specifications and hand them to Data Engineering (including its Technical Data Architects), who build the physical structures and pipelines.

• Review engineered outputs against the design; own the “what and how-connected,” while Engineering owns the build.

Cross-System & Cross-Domain Harmonisation

• Own [Procurement/Planning & Logistics/Manufacturing] deeply; harmonise definitions, business rules and master data at the seams with the other two domain Leads.

• Align your domain to the enterprise standards set with the Director, so the three domain maps compose into one coherent backbone.

Stakeholder Alignment & Translation

• Work with business SMEs, the GSC Data Office, Data Engineering (including Technical Data Architects) and the BI & AI teams to capture requirements, align definitions and validate models.

• Act as the translator between business meaning and canonical data structure — simplifying complex technical concepts for non-technical partners.

Standards, Tooling & Thought Leadership

• Set modelling standards, templates, metadata standards and lineage practices for the [Procurement/Planning & Logistics/Manufacturing] domain.

• Lead disciplined use of enterprise modelling tools (Erwin, ER/Studio, PowerDesigner) as a working expert.

Enable AI & Autonomous Planning

• Ensure models make [Procurement/Planning & Logistics/Manufacturing] data joinable and trustworthy enough for AI and autonomous-supply chain use cases — data a machine can act on.

• As the backbone matures (forward-looking): design enterprise semantic models that support analytics and AI/ML — a growth area for the role rather than a day-one focus.

Essential

• 8–12+ years of hands-on data modelling experience, operating as a senior working expert.

• Cross-system / integration / canonical modelling as your core strength — source-to-target mapping and bridging heterogeneous systems, not warehouse build alone.

• Deep understanding of how source systems structure data across a heterogeneous landscape — SAP ERPs plus best-of-breed and external systems (e.g. Kinaxis, Manhattan, procurement platforms, ERPs) 

• Master data / MDM literacy — comfortable designing around non-standard master data and coverage limitations.

• A partnering mindset on data quality and authoritative sources — able to translate business needs into backbone coverage and diagnose why data won’t join, working alongside the GSC Data Office.

• Grounding in relational, Kimball dimensional and Data Vault 2.0 modelling as foundations.

• Strong data governance instincts — metadata, lineage, definitions and standards.

Advantageous

• SQL fluency, and the ability to design for cloud data platforms (AWS, Databricks) and semantic layers — understanding and designing for these, rather than building them.

• Experience contributing to a data backbone, control tower or enterprise-integration programme.

Ways of Working

• Structured, detail-oriented, with a strong problem-solving mindset.

• A clear communicator who can simplify complex technical concepts for business and technical audiences alike.

• Proven ability to operate in a global, matrixed organisation and harmonise across domains.

WHY JOIN

• Build the connective tissue of the Data Backbone — the bridge that lets GSC run AI and autonomous planning now, ahead of full T.U.K convergence.

• Own an enterprise asset end to end: the map of how [Procurement/Planning & Logistics/Manufacturing] data connects across the supply chain.

• Shape enterprise-wide modelling standards from the ground up in a newly formed, strategically-backed team — turning disconnected data into a foundation that machines can reason on.

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