Data Engineer (Microsoft Fabric & Azure)

Location: Remote

Department: Managed Services

As one of Microsoft’s most recognized global partners, Quisitive sits at the forefront of cloud transformation, enterprise data strategy, cybersecurity, and the emerging frontier of agentic AI. Here, consultants and technologists operate on the edge of innovation—supported by a culture that values craftsmanship, open collaboration, and technical expertise. If you’re looking for a place where you can innovate, solve complex problems, and build solutions that make a measurable impact, join us.

As a Data Engineer, you will design, build, and maintain scalable data pipelines and platform solutions on Microsoft Azure. Working hands-on with services like Azure Databricks, Azure Synapse, and Microsoft Fabric, you’ll deliver robust data processing and ensure a stable, secure cloud environment that empowers analytics. The ideal candidate is a proficient Azure data engineer with a knack for automation and a collaborative mindset.

This is a remote position and can be located in the contiguous United States. Eastern or Central time zones preferred

Key Responsibilities:

• Design & Develop Data Pipelines: Build end-to-end data ingestion and transformation pipelines using Azure tools (e.g., Azure Data Factory, Synapse Pipelines). Ensure pipelines are efficient, reliable, and ready for continuous integration.
• Azure Databricks Development & Administration: Use Azure Databricks (Spark) to develop and optimize data processing jobs (PySpark/Scala) for large datasets. Manage Databricks workspaces and clusters, tuning configurations and troubleshooting jobs to maintain performance and cost-efficiency.
• Automate Infrastructure & Deployments: Use Infrastructure-as-Code (Bicep) to provision and manage Azure data platform resources across environments. Implement and maintain Azure DevOps CI/CD pipelines (build & release) for automated deployment of data pipelines and infrastructure changes.
• Modern Data Platform Support: Support the day-to-day operations of the Azure Modern Data Platform. Monitor and maintain platform stability (storage, compute), assist in resolving connectivity or networking issues, and ensure proper Azure RBAC permissions and data security measures are in place.
• Collaboration & Best Practices: Work closely with data architects, BI developers, cloud engineers, and client stakeholders. Ensure solutions align with Azure best practices, follow Quisitive standards, and meet business requirements.

 Required Experience:

• 3+ years of hands-on experience in data engineering with Microsoft Azure (data ingestion, ETL/ELT pipeline development, big data processing).
• Proficiency in Azure data services such as Azure Databricks, Azure Data Factory, Azure Synapse Analytics, and related SQL/NoSQL technologies.
• Experience using Azure DevOps (Repos & Pipelines) for CI/CD: managing code, building deployment pipelines, and automating releases.
• Practical experience with infrastructure-as-code (Bicep, ARM templates or Terraform) for deploying and managing Azure resources and environments.
• Strong programming skills in Python/Scala (for Spark jobs) and SQL (for data transformations).
• Familiarity with Microsoft Fabric and Azure fundamentals (cloud networking, security/permissions, monitoring) to support stable operations.
• Excellent problem-solving skills and effective communication when working with technical teams and clients.

 

US Citizens and those authorized to work in the US are encouraged to apply. We are unable to offer sponsorship at this time.

About Quisitive​

 

With significant growth since 2016, Quisitive is rapidly progressing our vision of becoming the leading global Microsoft partner as we continue to expand across the United States, Canada and India. With a diversified delivery model that includes both nearshore and offshore capabilities, our team of Microsoft experts delivers cloud solutions, artificial intelligence and business applications that transform our clients’ businesses and achieve remarkable business outcomes. ​