Role: Microsoft Data Architects – Fabric / ADF/Azure Data
Location: New York, NY / ONSITE
Duration: Long Term Project
Experience: 15+
Client: Capgemini/End client
Note: Plz don't share Data engineer resume.
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 Databricks / 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.?
Shahid Sahikh Senior Lead Technical Recruiter |
2050 Center Avenue | Suite 600 | Fort Lee, NJ 07024 Email: <a href="mailto:shahid.m@wonese.com" id="m_6230299025318361779OWA9a10b0e0-c2c5-1319-bd1f-d8cbedd8d1b6" title="shahid.m@wonese.com” style=”border:0px;font:inherit;margin:0px;padding:0px;vertical-align:baseline” target=”_blank”>shahid.m@wonese.com |
