The VAR Remediation experience is a big, big plus.

• Title:                   Azure Data Engineer
• Location:           NYC
• Long term
• 2 interviews
• Projected start date:  Early June
• local canddiates.
• Linledin and visa/DL 

As a seasoned Azure Data Engineer, your pivotal role revolves around the design, implementation, and optimization of data solutions within the Azure cloud ecosystem. You'll primarily concentrate on Azure Data Warehouse, harnessing your expertise in data engineering, Azure services, Python programming, and CI/CD methodologies to architect scalable data warehouses, fortify data integrity, and streamline data processing pipelines.

Key Responsibilities:

1. Data Warehouse Architecture Design: Collaborate closely with stakeholders to blueprint resilient and scalable data warehouse architectures on Azure, harnessing Azure SQL Data Warehouse alongside other pertinent Azure data services.
2. Azure Data Warehouse Implementation: Spearhead the execution of Azure Data Warehouse solutions, configuring data warehouses, delineating schema structures, and fine-tuning performance for large-scale data processing.
3. Data Ingestion and Integration: Craft and deploy data ingestion pipelines for seamlessly funneling data from diverse sources into Azure Data Warehouse, prioritizing data consistency, quality, and reliability. Employ Python for data pipeline orchestration and automation, incorporating VAR remediation/market data experience.
4. Data Transformation and ETL Processes: Engineer and refine ETL (Extract, Transform, Load) processes to transmute raw data into actionable insights within Azure Data Warehouse, leveraging Azure Data Factory, Azure Databricks, or related technologies. Python remains instrumental for data transformation tasks and scripting.
5. Data Modeling and Optimization: Devise and enact data models and schema designs tailored for Azure Data Warehouse, meticulously considering factors such as data distribution, partitioning, and indexing to enhance query performance. Python proficiency comes into play for data modeling and optimization algorithms.
6. Performance Tuning and Optimization: Proactively monitor and optimize the performance of Azure Data Warehouse, identifying and remedying performance bottlenecks, refining query execution, and optimizing resource utilization for heightened scalability and efficiency. Python scripts play a pivotal role in performance monitoring and optimization.
7. CI/CD Pipeline Development: Establish CI/CD pipelines for automated deployment, testing, and validation of data solutions on Azure, fostering reliability, repeatability, and consistency in solution delivery. Azure DevOps or similar CI/CD tools are instrumental for pipeline orchestration.
8. Monitoring and Troubleshooting: Implement robust monitoring and alerting mechanisms to vigilantly oversee the health and performance of Azure Data Warehouse, swiftly addressing issues to uphold high availability and reliability. Python aids in log analysis and troubleshooting automation.
9. Documentation and Knowledge Sharing: Document data warehouse architectures, design decisions, and implementation details comprehensively, actively contributing to knowledge dissemination within the team. Leverage Python-based documentation tools and utilities to enrich collaboration.

Qualifications: Include VAR remediation/market data experience in the list of qualifications.