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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Responsibilities
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.
