Senior Azure Data AnalystA 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 AnalystHow many years working with: Azure Data Factory (ADF)How many years working with: Azure Databricks (highlighted expertise)How many years working with: Logic AppsHow many years working with: Capital Markets/Hedge FundsTechnical 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 ServerAzure Data Factory (ADF)Azure Databricks (highlighted expertise)Azure Data Lake Storage (ADLS)Azure Key VaultAzure FunctionsLogic Apps5+ years of experience in GIT and deploying code using CI/CD pipelines.Certifications (Preferred):Microsoft Certified: Azure Data Engineer AssociateDatabricks Certified Data Engineer Associate or ProfessionalResponsibilities: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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