HAN
Data Architecture & Strategy :
Define enterprise-wide data architecture principles using Microsoft Fabric components (Data Lake, OneLake, Lakehouse, Warehouse, Real-Time Analytics). Align architecture with unified access, integration, and governance goals.
Microsoft Fabric Expertise :
Hands‑on understanding of Microsoft Fabric capabilities including Dataflows, Pipelines, Lakehouse, Warehouse, Notebooks, and integration with Power BI, Synapse, and Azure Data Factory.
Data Integration & App Connectivity :
Design data flows and connectors across operational systems and business applications. Enable seamless integration between transactional apps, analytics layers, and data science environments.
Proof‑of‑Concept Development :
Build quick, functional POCs using Fabric components to validate architecture decisions, performance, and usability before full‑scale implementation.
Unified Data Layer Design :
Architect a centralized but flexible data layer for consistent access and governance. Consider OneLake as the single data foundation across business units.
Data Governance & Security :
Define data ownership, metadata management, lineage tracking, and access control using Microsoft Purview and built‑in Fabric security models.
Scalability & Performance :
Ensure architecture is designed for large‑scale, real‑time analytics and app integrations. Plan for concurrent data access, transformation, and reporting across systems.
AI & Data Mining Enablement :
Incorporate AI‑driven analytics using Power BI AI visuals, notebooks (Python), and integration with Azure Machine Learning for predictive insights.
Stakeholder & Business Alignment :
Engage with enterprise data owners, analytics leaders, and app teams to ensure the solution meets business needs, is future‑ready, and simplifies the data landscape.
Delivery & Documentation :
Define architecture documentation, evaluation criteria, reference models, and rollout roadmap for implementation and adoption.
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Define enterprise-wide data architecture principles using Microsoft Fabric components (Data Lake, OneLake, Lakehouse, Warehouse, Real-Time Analytics). Align architecture with unified access, integration, and governance goals.
Microsoft Fabric Expertise :
Hands‑on understanding of Microsoft Fabric capabilities including Dataflows, Pipelines, Lakehouse, Warehouse, Notebooks, and integration with Power BI, Synapse, and Azure Data Factory.
Data Integration & App Connectivity :
Design data flows and connectors across operational systems and business applications. Enable seamless integration between transactional apps, analytics layers, and data science environments.
Proof‑of‑Concept Development :
Build quick, functional POCs using Fabric components to validate architecture decisions, performance, and usability before full‑scale implementation.
Unified Data Layer Design :
Architect a centralized but flexible data layer for consistent access and governance. Consider OneLake as the single data foundation across business units.
Data Governance & Security :
Define data ownership, metadata management, lineage tracking, and access control using Microsoft Purview and built‑in Fabric security models.
Scalability & Performance :
Ensure architecture is designed for large‑scale, real‑time analytics and app integrations. Plan for concurrent data access, transformation, and reporting across systems.
AI & Data Mining Enablement :
Incorporate AI‑driven analytics using Power BI AI visuals, notebooks (Python), and integration with Azure Machine Learning for predictive insights.
Stakeholder & Business Alignment :
Engage with enterprise data owners, analytics leaders, and app teams to ensure the solution meets business needs, is future‑ready, and simplifies the data landscape.
Delivery & Documentation :
Define architecture documentation, evaluation criteria, reference models, and rollout roadmap for implementation and adoption.
#J-18808-Ljbffr