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.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 .

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.

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.

Data Engineering builds to your designs and reviews with you, and the GSC Data Office owns fixing the source-data issues you surface. 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.

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.

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; 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 (explicit 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.

harmonise definitions, business rules and master data at the seams with the other two domain Leads.

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.

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

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.

8–12+ years of hands-on data modelling experience,



operating as a senior working expert.

- 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.

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.

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

It is designed to help you perform, grow, feel safe, and elevate your potential. The package helps you care for yourself and your family, plan your future, grow your skills and career, collaborate in an inclusive workplace, and contribute to your community. At Schneider Electric, our values and behaviors are the foundation for creating a great culture to support business success. We believe that our IMPACT values – Inclusion, Mastery, Purpose, Action, Curiosity, Teamwork – starts with us.

It is a call to connect your career with the ambition of achieving a more resilient, efficient, and sustainable world.

You must submit an online application to be considered for any position with us. We mirror the diversity of the communities in which we operate, and 'inclusion' is one of our core values. Our Trust Charter is our Code of Conduct and demonstrates our commitment to ethics, safety, sustainability, quality and cybersecurity, underpinning every aspect of our business and our willingness to behave and respond respectfully and in good faith to all our stakeholders. Schneider Electric is an Equal Opportunity Employer. It is our policy to provide equal employment and advancement opportunities in the areas of recruiting, hiring, training, transferring, and promoting all qualified individuals regardless of race, religion, color, gender, disability, national origin, ancestry, age, military status, sexual orientation, marital status, or any other legally protected characteristic or conduct.

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