DataOps Platform Engineer - Build Scalable Analytics

University of Vermont Medical Center

South Burlington (VT)

On-site

USD 110,000 - 160,000

Full time

4 days ago
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Job summary

The University of Vermont Medical Center seeks a DataOps / Platform Engineer to oversee the health, reliability, backup, and archiving of UVM Health's enterprise analytics data platform. The role ensures the Microsoft Fabric environment and related data infrastructure are stable, well-monitored, and optimized for a growing analytics organization.

You will lead the onboarding of new data sources, coordinating with business stakeholders and data engineers to assess feasibility and plan technical

Qualifications

  • Bachelor's degree in Computer Science, Information Systems, Data Engineering, or related field.
  • 4+ years of data platform operations, DataOps, or cloud infrastructure roles.
  • Experience with Microsoft Fabric, Azure infrastructure, or equivalent cloud data platforms.
  • Experience designing backup, archiving, and disaster recovery processes.
  • Familiarity with job scheduling, monitoring, and alerting tools.

Responsibilities

  • Ensure operational health, reliability, backups and archiving of the analytics platform.
  • Plan onboarding of new data sources with business stakeholders and data engineers.
  • Collaborate with teams to assess feasibility and plan technical integration.
  • Support agile product delivery and ongoing platform improvements.

Skills

DataOps
Cloud infrastructure
Monitoring
Alerting
Agile

Education

Bachelor's in CS/IS/Data Eng

Tools

Microsoft Fabric
Azure

Job description

The University of Vermont Medical Center seeks a DataOps / Platform Engineer to oversee the health, reliability, backup, and archiving of UVM Health's enterprise analytics data platform. The role ensures the Microsoft Fabric environment and related data infrastructure are stable, well-monitored, and optimized for a growing analytics organization.

You will lead the onboarding of new data sources, coordinating with business stakeholders and data engineers to assess feasibility and plan technical

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