Head of Data Engineering & Analytics
Pubblicato il 11-08-2026 - Altro 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
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