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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