Responsibilities
Verifichi qui sotto se possiede i requisiti per questa occasione e, in tal caso, invii la sua candidatura il prima possibile.

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- Define, maintain, and evolve the Enterprise Data Architecture blueprint, including core enterprise data domains (Customer, Product/Material, Vendor, Pricing, Finance, Trade Compliance)

- Conceptual and logical enterprise data models

- Canonical data definitions and semantics

- Act as Design Authority for all data‑related initiatives, ensuring alignment with enterprise architecture principles

- Define and enforce enterprise data standards (modeling, naming, semantics, integration)

- Collaborate closely with Enterprise, Solution, and Integration Architects

- Own the enterprise Master Data Management (MDM) strategy and execution roadmap

- Define domain prioritization (e.g. Customer, Product/Material) and rollout phases

- Establish golden record, survivorship, hierarchy, and relationship‑management rules

- Lead the design and implementation governance of the selected MDM platform

- Oversee data migration, cleansing, and harmonization activities linked to MDM adoption

- Establish and run the Enterprise Data Governance operating model, including data ownership and stewardship framework

- Governance forums, decision bodies, and escalation mechanisms

- Define and monitor enterprise data quality rules and KPIs (completeness, accuracy, uniqueness, timeliness)

- Implement structured data issue management and remediation processes

- Ensure data lineage, traceability, and auditability for critical business and regulatory data

- Define and maintain System‑of‑Record (SoR) / System‑of‑Engagement (SoE) principles across SAP (ERP, BW, GTS, Concur), Salesforce CRM,



MES systems (e.g. Promis, Critical Manufacturing), Quoting and pricing platforms, Finance, Treasury, AP automation, and EDI solutions

- Resolve data ownership conflicts and duplication at enterprise level

- Ensure consistent and governed data synchronization patterns across systems

- Define enterprise canonical data models and data contracts for cross‑system integrations

- Govern data flows implemented via SAP BTP, MuleSoft, and EDI platforms

- Define integration patterns (API, event‑driven, batch, EDI) from a data semantics, integrity, and lifecycle standpoint

- Ensure interface versioning discipline and backward compatibility

- Ensure conformed dimensions and consistent master data usage across analytics and planning platforms (e.g. SAP BW)

- Align enterprise data definitions with KPIs, reporting, and planning use cases

- Prevent multiple and conflicting versions of enterprise truth

- Translate architectural and governance decisions into executable implementation backlogs

- Review technical designs and ensure adherence to enterprise standards

- Coordinate delivery with internal teams and external partners

Requirements

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- Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field

- 10+ years of experience in Enterprise Data Architecture, Data Governance,



or related roles

- Demonstrated experience in SAP‑centric enterprise landscapes integrated with multiple non‑SAP platforms

- Proven experience designing and governing Master Data Management solutions

- Strong background in enterprise integration concepts and data exchange patterns

- Experience operating in complex, global, and regulated environments

- Enterprise data modeling (conceptual, logical, canonical)

- Data governance frameworks and stewardship models

- Master data and reference xysqume data management

- Data quality frameworks, metrics, and lifecycle management

- Integration data semantics (API‑led, event‑driven, batch, EDI)

- Solid understanding of SAP data concepts (Business Partner, Material, Finance, Pricing, BOMs, Routings)

- Strong stakeholder management, facilitation, and decision‑making skills

- Clear and structured documentation and communication

- Manufacturing and MES data architecture

- Quote‑to‑Cash and pricing data domains

- Trade compliance and regulatory master data (e.g. export control, classification)

- Analytics and planning data architecture

- Experience leading small technical or architecture teams

- Delivery focused with previous experience on at least one major data lake transition

- Enterprise data ownership and governance model formally established and adopted

- Measurable improvement in master data quality and reduction in duplicates

- Successful MDM/MDG rollout for prioritized domains

- Stable, reusable, and well‑governed data integration patterns

- Improved auditability, compliance, and reporting consistency

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📌 Head of Data Engineering & Analytics (Torino)
🏢 Jobtailor
📍 Torino