Job Description

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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