Business Intelligence Architect (Azure Synapse, MS Fabric, Power BI)

Job Responsibilities

• Data Architecture & Engineering:
• Design and implement large-scale data architectures using Azure Synapse Analytics and Microsoft Fabric to support data warehousing, real-time analytics, and big data processing.
• Develop and maintain data pipelines for ETL/ELT processes, ensuring data quality, security, and integrity.
• Create data solutions that integrate with Power BI, enabling advanced analytics and reporting across the organization.
• Azure Synapse Development:
• Build and optimize data pipelines in Azure Synapse to handle high volumes of structured and unstructured data.
• Design, implement, and optimize distributed data processing solutions using Azure Synapse for large-scale analytics.
• Manage and maintain Azure Synapse workspaces, Data Lakes, and other associated resources.
• Microsoft Fabric Implementation:
• Leverage Microsoft Fabric for data integration and orchestration across cloud and on-premises environments.
• Design and implement hybrid data management solutions using Microsoft Fabric to optimize data workflows and business processes.
• Collaborate with data teams to create reusable data assets and connectors that integrate with various sources and Power BI.
• Power BI Integration:
• Work closely with BI teams to ensure seamless integration between Azure Synapse datasets and Power BI for real-time reporting.
• Build, optimize, and manage Power BI datasets, ensuring efficient performance for complex reports and dashboards.
• Support BI developers in designing data models and optimizing DAX queries for Power BI.
• Collaboration & Stakeholder Management:
• Collaborate with data scientists, analysts, and business stakeholders to gather requirements and design data solutions that meet evolving business needs.
• Communicate complex technical concepts and data architectures to non-technical stakeholders in a clear and understandable manner.
• Data Governance & Best Practices:
• Implement and enforce data governance policies, ensuring compliance with data privacy, security, and regulatory requirements.
• Develop and maintain best practices for data engineering, including version control, testing, and documentation.
• Monitor and optimize data performance, scalability, and cost-efficiency in cloud environments.

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