Head of Data Engineering & Analytics

Jobtailor; Torino; In loco; EUR 110.000 - 150.000; Tempo pieno

Descrizione del lavoro

Jobtailor is seeking an Enterprise Data Architect to define and evolve the data architecture across key domains, lead the MDM strategy, and drive governance and data quality initiatives. You will partner with SAP, CRM, MES, and other platforms to ensure consistent data definitions and scalable integration.

The role requires 10+ years in data architecture with SAP-centric experience, strong modeling skills, and proven governance implementations in global, regulated environments.

Competenze

• 10+ years in Enterprise Data Architecture or related roles.
• Experience with SAP-centric landscapes integrated with non-SAP systems.
• Proven Master Data Management design and governance experience.
• Strong data modeling, integration, and data quality practices.
• Experience in regulated, global environments and data lineage.

Mansioni

• Define, maintain, and evolve the enterprise data architecture blueprint across core domains.
• Lead the MDM strategy, domain prioritization, and rollout roadmap.
• Establish data governance operating model, ownership, and KPIs.
• Define standards for modeling, naming, semantics, and integration.
• Oversee data migration, cleansing, and harmonization tied to MDM adoption.
• Govern data quality rules, lineage, and auditability for critical data.
• Coordinate with SAP, Salesforce, MES, and other systems; review designs for standards adherence.
• Lead small technical/architecture teams and translate decisions into backlogs.

Conoscenze

Data Architecture

Data Governance

Master Data Management

Data Modeling

Data Quality

Data Integration

Stakeholder Management

Regulatory Compliance

Formazione

Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering

Strumenti

SAP

SAP BW

MuleSoft

EDI platforms

Descrizione del lavoro

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