Head of Data Engineering & Analytics
Pubblicato il 11-08-2026 - Jobtailor in Torino
ph3Responsibilities /h3 ul liDefine, maintain, and evolve the Enterprise Data Architecture blueprint, including core enterprise data domains (Customer, Product/Material, Vendor, Pricing, Finance, Trade Compliance) /li liConceptual and logical enterprise data models /li liCanonical data definitions and semantics /li liAct as Design Authority for all data‑related initiatives, ensuring alignment with enterprise architecture principles /li liDefine and enforce enterprise data standards (modeling, naming, semantics, integration) /li liCollaborate closely with Enterprise, Solution, and Integration Architects /li liOwn the enterprise Master Data Management (MDM) strategy and execution roadmap /li liDefine domain prioritization (e.g. Customer, Product/Material) and rollout phases /li liEstablish golden record, survivorship, hierarchy, and relationship‑management rules /li liLead the design and implementation governance of the selected MDM platform /li liOversee data migration, cleansing, and harmonization activities linked to MDM adoption /li liEstablish and run the Enterprise Data Governance operating model, including data ownership and stewardship framework /li liGovernance forums, decision bodies, and escalation mechanisms /li liDefine and monitor enterprise data quality rules and KPIs (completeness, accuracy, uniqueness, timeliness) /li liImplement structured data issue management and remediation processes /li liEnsure data lineage, traceability, and auditability for critical business and regulatory data /li liDefine 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 /li liResolve data ownership conflicts and duplication at enterprise level /li liEnsure consistent and governed data synchronization patterns across systems /li liDefine enterprise canonical data models and data contracts for cross‑system integrations /li liGovern data flows implemented via SAP BTP, MuleSoft, and EDI platforms /li liDefine integration patterns (API, event‑driven, batch, EDI) from a data semantics, integrity, and lifecycle standpoint /li liEnsure interface versioning discipline and backward compatibility /li liEnsure conformed dimensions and consistent master data usage across analytics and planning platforms (e.g. SAP BW) /li liAlign enterprise data definitions with KPIs, reporting, and planning use cases /li liPrevent multiple and conflicting versions of enterprise truth /li liTranslate architectural and governance decisions into executable implementation backlogs /li liReview technical designs and ensure adherence to enterprise standards /li liCoordinate delivery with internal teams and external partners /li /ul h3Requirements /h3 ul liBachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field /li li10+ years of experience in Enterprise Data Architecture, Data Governance,
or related roles /li liDemonstrated experience in SAP‑centric enterprise landscapes integrated with multiple non‑SAP platforms /li liProven experience designing and governing Master Data Management solutions /li liStrong background in enterprise integration concepts and data exchange patterns /li liExperience operating in complex, global, and regulated environments /li liEnterprise data modeling (conceptual, logical, canonical) /li liData governance frameworks and stewardship models /li liMaster data and reference data management /li liData quality frameworks, metrics, and lifecycle management /li liIntegration data semantics (API‑led, event‑driven, batch, EDI) /li liSolid understanding of SAP data concepts (Business Partner, Material, Finance, Pricing, BOMs, Routings) /li liStrong stakeholder management, facilitation, and decision‑making skills /li liClear and structured documentation and communication /li liManufacturing and MES data architecture /li liQuote‑to‑Cash and pricing data domains /li liTrade compliance and regulatory master data (e.g. export control, classification) /li liAnalytics and planning data architecture /li liExperience leading small technical or architecture teams /li liDelivery focused with previous experience on at least one major data lake transition /li liEnterprise data ownership and governance model formally established and adopted /li liMeasurable improvement in master data quality and reduction in duplicates /li liSuccessful MDM/MDG rollout for prioritized domains /li liStable, reusable, and well‑governed data integration patterns /li liImproved auditability, compliance, and reporting consistency /li /ul /p #J-18808-Ljbffr