Solution Data Architect

• Strong background in enterprise data architecture and cloud-based data platforms.
• Practical experience with platforms such as Azure, Databricks, Microsoft Fabric, AWS, or similar ecosystems.
• Experience designing data lakes, data warehouses, lakehouses, integration layers, and analytics platforms.
• Strong understanding of data governance, security, access management, compliance, and data residency considerations.
• Ability to quickly assess complex and fragmented enterprise landscapes.
• Experience defining target architectures and turning technical findings into actionable recommendations.
• Strong communication and stakeholder management skills, especially in client-facing or advisory environments.
• Ability to balance strategic architecture thinking with practical implementation feasibility.

Nice to have:

• Experience in manufacturing, logistics, supply chain, finance, or multi-site enterprise environments.
• Experience integrating or assessing systems such as ERP, CRM, MES, reporting platforms, or operational systems.
• Exposure to AI readiness, ML/AI platforms, GenAI enablement, data products, or modern analytics operating models.
• Experience preparing roadmaps, architecture recommendations, implementation plans, or high-level delivery sizing.

• Assess the current technology and data landscape, including source systems, data flows, integrations, reporting flows, and architectural constraints.
• Identify data fragmentation, architecture gaps, platform risks, dependencies, and areas requiring improvement.
• Define the target-state data platform architecture, including platform building blocks, integration patterns, data movement principles, and operating model considerations.
• Evaluate platform options such as Azure, Microsoft Fabric, Databricks, AWS, or comparable technologies.
• Recommend platform and toolchain direction based on business value, technical fit, scalability, cost, EU data residency, security, and operational feasibility.
• Support AI readiness assessment from an architecture and data foundation perspective.
• Provide input into data governance, access management, security, compliance, and data ownership principles.
• Assess the technical feasibility of prioritized business and AI use cases.
• Support the Phase 2 roadmap, including implementation sequencing, high-level sizing, and practical recommendations for delivery.
• Work closely with business and technical stakeholders to translate findings into clear architecture decisions and next steps.