Azure Data Engineer

Senior Azure Data Analyst

A senior Azure Data Analyst with extensive experience working with the Azure Data suite listed below. The client would like to see certifications.

Candidates must have recent capital markets/trading and/or hedge fund experience and excellent communication skills. Hot & moving fast!

Candidate must have's on a resume and for submittal:

• How many years working with: Azure Data Analyst
• How many years working with: Azure Data Factory (ADF)
• How many years working with: Azure Databricks (highlighted expertise)
• How many years working with: Logic Apps
• How many years working with: Capital Markets/Hedge Funds
• Technical skills:
• Programming & tools: 10+ years of experience in SQL, Python..Net is a plus.
• 5+ years of experience in Azure cloud services, including:
• Azure SQL Server
• Azure Data Factory (ADF)
• Azure Databricks (highlighted expertise)
• Azure Data Lake Storage (ADLS)
• Azure Key Vault
• Azure Functions
• Logic Apps
• 5+ years of experience in GIT and deploying code using CI/CD pipelines.
• Certifications (preferred):
• Microsoft Certified: Azure Data Engineer Associate
• Databricks Certified Data Engineer Associate or Professional

Responsibilities:

1. Data Pipeline Development:

• Create and manage scalable data pipelines to collect, process, and store large volumes of data from various sources.

• Data Integration:

• Integrate data from multiple sources, ensuring consistency, quality, and reliability.

• Database Management:

• Design, implement, and optimize database schemas and structures to support data storage and retrieval.

• ETL Processes:

• Develop and maintain ETL (Extract, Transform, Load) processes to ensure accurate and efficient data movement between systems.

• Data Warehousing:

• Build and maintain data warehouses to support business intelligence and analytics needs.

• Performance Optimization:

• Optimize data processing and storage performance for efficient resource utilization and quick data retrieval.

• Documentation:

• Create and maintain comprehensive documentation for data pipelines, ETL processes, and database schemas.

• Monitoring and Troubleshooting:

• Monitor data pipelines and systems for performance and reliability, troubleshooting and resolving issues as they arise.

• Technology Evaluation: