Azure Data Engineer

Azure Data EngineerDallas TX (Hybrid 3 days in a week) 12+ Months Web Cam Interview Requirement Notes (Candidate Job description below):We need a senior (10+ years) Azure Data engineer with recent experience in Banking, Capital Markets or Financial services. Candidates must have recent experience working with Azure Data Factory (ADF) and Azure Databricks in a Financial environment. Candidates must have excellent communication skills/no accent.Experience required on a resume and for submittal:How many years working with: Azure Data EngineerHow many years working with: Azure Data Factory (ADF)How many years working with: Azure Databricks (highlighted expertise)How many years working with: Banking, Capital Markets or Financial services OR FORTUNE 500Education:Bachelor's or Master's degree in Computer Science, Information Technology, or a related field (Engineering or Math preferred).Technical Skills: Programming & Tools:10+ years of experience in SQL, Python..Net is a plus.3+ 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 Apps3+ 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 ProfessionalSoft Skills:Strong analytical and problem-solving skills.Excellent communication and interpersonal skills.Ability to work independently and collaboratively within a team.Attention to detail and a commitment to delivering high-quality work.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 new tools and technologies as appropriate.