What are the responsibilities and job description for the Azure Data Engineer/ Architect with Fabric position at Sysmind LLC ?
Position : Azure Data Engineer / Architect with Fabric
Location : New Jersey, NJ (Onsite)
Duration : Long Term
Job Description :
Deep expertise in modern data architecture, with specific experience in Microsoft's data platform and Delta Lake architecture. 6 years of experience in data architecture and engineering. Required 2 years hands-on experience with Azure Data bricks / ADF and Spark. Required recent experience with Microsoft Fabric platform. Key Responsibilities :
Data Architecture :
Design end-to-end data architecture leveraging Microsoft Fabric's capabilities. Design data flows within the Microsoft Fabric environment. Implement OneLake storage strategies. Configure Synapse Analytics workspaces. Establish Power BI integration patterns. Integration Design :
Architect data integration patterns with analytics using Azure Data Factory and Microsoft Fabric. Implement medallion architecture (Bronze / Silver / Gold layers). Ability to configure real-time data ingestion patterns. Establish data quality frameworks. Lakehouse Architecture :
Implement modern data lakehouse 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. Analytics 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 Azure Databricks Experience :
Apache 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 Databricks clusters for optimized performance. (Ex : autoscaling and automatic termination) Integration with Azure Services :
Integrating Databricks with other Azure services like Azure Data Lake, Azure SQL Database, and Azure Synapse Analytics. Data Governance :
Implementing data governance practices using Unity Catalog and Microsoft Purview Security Framework :
Design and implement security patterns aligned with federal and state requirements for sensitive data handling. Implement row-level security. Configure Microsoft Purview policies. Establish data masking for sensitive information. Design audit logging mechanisms. Pipeline Development :
Design scalable data pipelines using Azure Databricks 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.
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