Job Openings Azure Data Engineer - 100% Remote - Nightshift -200K

About the job Azure Data Engineer - 100% Remote - Nightshift -200K

Position: Senior Data Engineer (SQL Developer with strong Azure cloud experience)

Work setup & shift: Work from Home | Nightshift

Salary is up to 200k based on experience.

Technical Expertise:
5+ years Azure experience (SQL MI, Data Factory, DevOps)
8+ years SQL development (T-SQL, SSIS)
Azure Functions

Preferred Certifications:
Microsoft Certified: Azure Data Engineer Associate

Key Requirements:
- Hands-on experience with Azure Data Factory, including pipeline orchestration
- Azure Functions using C#
- SQL Server Management experience
- Understanding of DevOps practices
- Strong, in-depth SQL expertise
- 5+ years Azure experience (SQL Managed Instances, Data Factory, DevOps)
- Strong SQL development experience, preferably 8 years of experience

- Microsoft Certified: Azure Data Engineer Associate (preferred)

Your Role:

This position will play a pivotal role in designing, developing, and optimizing data solutions within Azure cloud environments. This position requires a strong SQL developer with extensive experience in Azure Managed Instances, Azure Data Pipelines, and cloud architecture. The ideal candidate will leverage their analytical skills to transform raw data into actionable insights, ensuring scalability, efficiency, and adherence to data governance standards.

SQL Development & Optimization:

  • Design and develop complex SQL queries, stored procedures, and functions
  • Optimize T-SQL scripts and database schemas for performance
  • Perform database tuning, indexing, and partitioning

Azure Cloud & Managed Instances:

  • Architect and manage Azure SQL Managed Instances
  • Implement Azure Data Factory pipelines with Azure DevOps CI/CD
  • Utilize Azure Synapse and Azure Databricks for analytics (preferred
Data Pipeline & Architecture:
  • Build scalable data pipelines using Azure Data Factory and SSIS Analytical & Problem-Solving:
  • Analyze data trends for process improvement
  • Troubleshoot production issues and performance bottlenecks
  • Develop predictive and prescriptive models