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: 1. How many years working with: Azure Data Analyst 2. How many years working with: Azure 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: