Job Description

senioryears ) 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: • 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: Stay updated with emerging technologies and best practices in data engineering, evaluating and recommending

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