Head of Data Engineering & Analytics - Torino, Piemonte, Italy
Pubblicato il 20-08-2026 - Jobtailor in Torino
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 #J-18808-Ljbffr