Azure Fabric Data Architect

Job Description:

Pay Range: $75hr - $80hr

Responsibilities:

Data Architecture:

• Design end-to-end data architecture leveraging Microsoft Fabric's capabilities.
• Design data flows within the Microsoft Fabric environment.
• Implement One Lake storage strategies.
• Configure Synapse Analytics workspaces Establish Power BI integration patterns.

Integration Design:

• rchitect data integration patterns with analytics using Azure Data Factory and Microsoft Fabric.
• Implement medallion architecture (Bronze/Silver/Gold layers).
• bility to configure real-time data ingestion patterns.
• stablish data quality frameworks.

Lakehouse Architecture:

• Implement modern data lake house architecture using Delta Lake, ensuring data reliability and performance.

Data Governance:

• Establish data governance frameworks incorporating Microsoft Purview for data quality, lineage, and compliance.

Microsoft Fabric Expertise:

• Data Integration: Combining and cleansing data from various sources.
• Data Pipeline Management: Creating, orchestrating, and troubleshooting data pipelines.
• nalytics Reporting: Building and delivering detailed reports and dashboards to derive meaningful insights from large datasets.
• Data Visualization Techniques: Representing data graphically in impactful and informative ways.
• Optimization and Security: Optimizing queries, improving performance, and securing data.

zure Databricks Experience:

• pache Spark Proficiency: Utilizing Spark for large-scale data processing and analytics.
• Data Engineering: Building and managing data pipelines, including ETL (Extract, Transform, Load) processes.
• Delta Lake: Implementing Delta Lake for data versioning, ACID transactions, and schema enforcement.
• Cluster Management: Configuring and managing Data bricks clusters for optimized performance.
• Integration with Azure Services: Integrating Data bricks with other Azure services like Azure Data Lake, Azure SQL Database, and Azure Synapse Analytics.
• Configure Microsoft Purview policies.
• Establish data masking for sensitive information.
• Design audit logging mechanisms.

Pipeline Development:

• Design scalable data pipelines using Azure Data bricks for ETL/ELT processes and real-time data integration.

Performance Optimization:

• Implement performance tuning strategies for large-scale data processing and analytics workloads.
• Optimize Spark configurations.
• Implement partitioning strategies
• Design caching mechanisms.
• Establish monitoring frameworks.