Solution Data Architect
Summary
Designs and advises on enterprise-scale data platforms, defining target architectures for cloud-based data lakes, warehouses, and analytics systems while ensuring governance, security, and AI-readiness.
We are looking for an experienced Data Platform Architect to lead the technical discovery and architecture workstream for a future-ready enterprise data platform. The role focuses on assessing the current data landscape, defining the target architecture, and providing practical recommendations for platform direction, integration patterns, governance, security, and the next phase of implementation.
This is a client-facing role combining enterprise data architecture, cloud platform expertise, and strategic advisory. You will help shape the “North Star” architecture and ensure the platform is scalable, secure, cost-effective, compliant, and ready to support advanced analytics and AI use cases.
Who we are looking for
We are looking for someone who can bring structure and clarity to a complex data environment. This role will suit an architect who can quickly understand fragmented systems, assess platform options objectively, and define a pragmatic target architecture that supports both current business needs and future AI-driven capabilities.
- 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.