Senior IT Systems Engineer

Fullstory•Atlanta, GA

5d•$115,000 - $125,000•Hybrid

About The Position

Fullstory is looking for an IT Systems Engineer to join their team. This role is responsible for the tools and systems that keep the company operational, including identity management, device management, collaboration platforms, and automation. The position requires the ability to work across a variety of technologies and problems, learn new tools and processes quickly, and treat ambiguity as a starting point. The role is hybrid, requiring one day per week in the Atlanta office, and candidates must reside within a commutable distance.

Requirements

5+ years of experience in IT systems engineering or administration
Hands-on Okta administration experience, ideally including Okta Workflows or similar automation platform
Experience with a MacOS MDM platform (Iru, Jamf, or similar), including scripting against its API
Administrative experience with Atlassian tools, especially Confluence and Jira
Scripting ability in Python and Bash, comfortable enough to build and maintain real automation
Real, hands-on experience integrating AI tools into enterprise workflows (MCP connectors, AI coding tools, or similar)
A bachelor's degree in a related field.

Responsibilities

Administer Okta and build automation in Okta Workflows, including SSO app configuration, provisioning/deprovisioning, group-based triggers, and troubleshooting errors.
Own the MacOS fleet using an MDM (like Iru or Jamf), including blueprints, self-service items, compliance scripting (FileVault, SIP, secure token, activation lock, MDM status), and API-driven automation for device lifecycle management.
Write and maintain bash and Python scripts for pre/post-install packaging, endpoint automation, and compliance checks, adhering to SDLC standards.
Administer the Atlassian suite (Confluence and Jira), including writing internal process documentation, triaging tickets, and debugging automation tied to ticket transitions and workflow triggers.
Set up and maintain AI tooling in the enterprise stack, including configuring MCP connectors for AI models, building self-service setup scripts for developer AI tools, and understanding data compliance for AI tools.