Senior Data & Analytics Product Specialist

Job Main Responsibilities

• Design and maintain business dashboards
• Translate business strategy into analytics roadmap
• Enable business performance insights
• Act as analytics partner for business
• Lead cross-domain alignment

Technical Skills & Technology Landscape

• Power BI-type BI platforms
• Enterprise semantic models
• Advanced business KPIs
• Data visualization and UX best practices
• Collaboration with Data Engineering teams

Qualifications And Skills

• Core Technical
• Advanced Power BI development: composite models, RLS, deployment pipelines, Premium features
• Enterprise semantic modelling in Microsoft Fabric /Databricks, Power BI Premium for supply chain domains
• SAP supply chain data architecture: extraction from MM/PP/SD/WM, delta patterns, reconciliation (preferred)
• business analytics roadmap: KPI taxonomy, domain data product ownership
• Predictive analytics enablement
• Self-service analytics governance: framework design, training, adoption management
• Integration with Databricks Gold layer for advanced supply chain data products
• Certifications
• Microsoft Certified: Power BI Data Analyst Associate - Mandatory
• Microsoft Certified: Fabric Analytics Engineer Associate - Strongly Preferred
• SAP Certified Application Associate - Procurement / Materials Management - Strongly Preferred
• APICS CSCP or CLTD (Logistics, Transportation & Distribution) - Strongly Preferred
• Microsoft Certified: Azure Data Engineer Associate - Preferred
• Industry & Business Knowledge
• Deep SAP supply chain knowledge: MM, PP, SD, WM / EWM, APO / IBP
• Advanced supply chain KPI ownership: OTIF, DIFOT, inventory turns, working capital impact
• S&OP and integrated business planning data requirements
• Industrial minerals supply chain: quarry-to-customer data flows, logistics costing
• Cross-domain coordination: alignment with finance (COGS, stock valuation) and operations
• Behavioral & Leadership
• Supply chain business partnering - acts as analytics advisor to SCM leadership
• Domain data product ownership: accountable for quality, adoption, and evolution
• Strong prioritisation: manages competing demands from planning, logistics, and procurement
• Translates operational complexity into clear analytical insight
• Cross-functional leadership: aligns data engineering, governance, and business stakeholders

Let op: vacaturefraude

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Important: job fraud

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