Data Solutions Architect

Data Solutions Architect

Required Skills & Experience: 10+ years of experience in Solution Architecture, Data Architecture, or Cloud Architecture roles. Strong experience designing enterprise-scale analytics and data platform solutions using Microsoft Azure. Demonstrated experience with Microsoft Fabric architecture, capabilities, and implementation patterns. Experience designing batch, near real-time, and real-time data and analytics solutions. Strong understanding of data integration, data engineering, analytics, reporting, and self-service data platform capabilities. Experience collaborating with business stakeholders to transform requirements into executable solution architectures. Experience working closely with networking, infrastructure, security, and platform engineering teams. Ability to create high-quality architecture documentation and lead architecture approval processes.

Job Description: A well known utilities company is seeking an experienced Data Solutions Architect to support the evolution of our enterprise data and analytics platform. The organization has already established a foundational Microsoft Fabric strategy and platform. This role is focused on extending and tailoring that foundation to support specific business domains with evolving analytical, operational, self-service, near real-time, and potentially real-time data requirements. The architect will partner closely with business stakeholders, technical leads, enterprise architects, security teams, networking teams, and engineering resources to translate business objectives into scalable, secure, and future-ready architecture designs. A key responsibility of this role is to take existing discovery work and business requirements, validate and refine those requirements through stakeholder engagement, identify capability gaps, and design end-to-end solutions that leverage Microsoft Fabric and Azure services where appropriate. The architect must ensure proposed solutions not only satisfy current requirements but also accommodate future growth, data volume increases, self-service analytics needs, and emerging real-time use cases.