Stellar Professionals LLC

Stellar Professionals LLC

Azure Devops Engineer

Harrisburg, Pennsylvania, USA · Part-time

Sponsorship not specifiedDetected 60 days ago
JavaNode.jsGitSQLAzureCloud PlatformsTerraformCI/CDDevOpsCybersecurityMentoringPipeline Integrity

About the role

  • Position Details Location: Harrisburg, PA (Dauphin County, 17101) Work Arrangement: Part-Time Hybrid Telework Months 1 3: 2 days/week in-office (Wednesday & Thursday).
  • Month 4 onwards: 1 day/month in-office (First Wednesday of each month).
  • Schedule: Full-time, 40 hours/week (Monday Friday, 8:30 AM 5:00 PM EST).

Responsibilities

  • Administer and optimize Azure DevOps Services and GitHub Enterprise (managing RBAC, SSO, repositories, and branch policies).
  • Design and maintain secure, automated YAML-based CI/CD pipelines in both Azure Pipelines and GitHub Actions.

Nice to have

  • Microsoft Certifications ( AZ-400, AZ-104, or AZ-305).

Skills

  • 5+ Years of experience in Azure DevOps as both a Developer and an Administrator.
  • Deep hands-on experience administering GitHub Enterprise (Cloud or Server).
  • Strong expertise in YAML pipelines, Git branching strategies, and CI/CD best practices.
  • Solid understanding of Infrastructure as Code (IaC) using Terraform, Bicep, or ARM templates.
  • Proficient with scripting languages like PowerShell.
  • Experience supporting cloud environments within regulated, policy-driven, or compliance-heavy settings.
  • Highly Desired (Big Plus): Microsoft Certifications ( AZ-400, AZ-104, or AZ-305).
  • GitHub Certifications (Foundations, Actions, or Administration).
  • Experience with Azure Monitor, App Insights, and Log Analytics.
  • Background in.NET, Java, or Node.js ecosystems.
  • Important Compliance Notes Candidates must currently reside in or be willing to commute to Harrisburg, PA for hybrid days.
  • A secure, high-speed internet connection is required for remote work days.

This listing is sourced directly from Stellar Professionals LLC's careers page and normalized into a canonical job model.