Responsibilities - 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 - 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 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

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