Azure Data Engineer

senior (1 2 + years ) 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:

1. How many years working with: zure Data Analyst

2. How many years working with: zure Data Factory (ADF

3. How many years working with: Azure Databricks (highlighted expertise)

4. How many years working with: Logic Apps

5. 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.
2. Data Integration:
   • Integrate data from multiple sources, ensuring consistency, quality, and reliability.
3. Database Management:
   • Design, implement, and optimize database schemas and structures to support data storage and retrieval.
4. ETL Processes:
   • Develop and maintain ETL (Extract, Transform, Load) processes to ensure accurate and efficient data movement between systems.
5. Data Warehousing:
   • Build and maintain data warehouses to support business intelligence and analytics needs.
6. Performance Optimization:
   • Optimize data processing and storage performance for efficient resource utilization and quick data retrieval.
7. Documentation:
   • Create and maintain comprehensive documentation for data pipelines, ETL processes, and database schemas.
8. Monitoring and Troubleshooting:
   • Monitor data pipelines and systems for performance and reliability, troubleshooting and resolving issues as they arise.
9. Technology Evaluation:

Stay updated with emerging technologies and best practices in data engineering, evaluating and recommending