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