Systems Engineer

Job Description

Job Description

What You'll Own — Operational Backbone

• Keep the execution engine running with the rigor our launches demand.
• Atlassian platform: administration, configuration, and governance of Jira and Confluence — projects, workflows, permissions, templates, and launch-tracking structures.
• SharePoint & content management: architecture, permissions, document management, and site governance across the collaboration and knowledge environment.
• Office & workflow automation: build and maintain templates, macros, and integrations that standardize how launch status, readiness, and reporting are captured and shared.
• Governance & reliability: own data hygiene, access control, and change management so the single source of truth stays trustworthy and audit-ready.

What You'll Build — AI Transformation & Intelligent Automation Lead the shift from manual tracking to AI-augmented execution across the GTM function.

• Build agents on Claude: design and build AI agents on Claude (Anthropic API/SDK, primarily in Python) that integrate with our Jira, Confluence, and SharePoint data to eliminate manual status collection, reminders, and report assembly. Note: our Atlassian environment is self-managed (not Atlassian Cloud), so agentic capability is built on Claude as a separate, dedicated layer rather than relying on built-in cloud AI features.
• Intelligent knowledge access: stand up AI-assisted, retrieval-augmented search across Confluence and SharePoint content so launch teams instantly surface decisions, specs, playbooks, and prior-launch learnings — powered by Claude over our own document base.
• Decision-ready visibility: build launch-readiness dashboards and early-warning risk flags that turn scattered execution data into a real-time view of timeline and dependency health.
• AI tooling & governance: evaluate, pilot, and govern the Claude-based agents and assistants, and define responsible-use guardrails and data-handling standards as adoption grows across the GTM org.
• Cross-team enablement: partner with Business Operations and Strategic Operations to automate reporting inputs and BI feeds, reducing manual effort in forecasting and performance tracking.
• Own the roadmap: identify high-ROI AI use cases, prototype quickly, measure impact, and scale what works — acting as the transformation catalyst for how the team operates.

Required Qualifications

• 5+ years administering Atlassian (Jira / Confluence) and SharePoint in an operations, or product environment.
• Hands-on programming ability — Python for automation, data wrangling, and API integration; comfort with REST APIs, webhooks, and scripting. (This is a genuine upgrade over a pure-admin profile: the role requires someone who can build, not only configure.)
• Demonstrated experience building workflow automation (Jira automation, custom scripting, or equivalent) that measurably reduced manual effort.
• Hands-on experience building AI agents and agentic workflows — orchestrating LLM calls, tools, and multi-step reasoning — ideally on Claude via the Anthropic API/SDK. Solid grounding in prompt engineering, retrieval-augmented generation, and connecting agents to real systems (Jira, Confluence, SharePoint) through their APIs.
• Data and reporting depth — able to build dashboards and models using SQL, Python, and modern BI / reporting tools.
• Strong process orientation — able to translate messy operational needs into reliable, documented, maintainable systems.

Preferred Qualifications

• Experience in a go-to-market, product-launch, or PMO environment operating at scale.
• Exposure to Java or another object-oriented language for deeper integration and custom tooling.
• Experience defining AI governance or responsible-use policy for a team or function.
• Change-management and enablement experience — training non-technical teams to adopt new tools and ways of working.
• Relevant certifications (PMP, Atlassian, or applied-AI credentials).

First 6–12 Months — What Success Looks Like

• Stabilized and documented the Jira / Confluence / SharePoint environment with clear governance.
• Shipped 2–3 automations that measurably cut manual launch-tracking and reporting effort.
• Stood up AI-assisted search across the knowledge base, adopted by launch teams.
• Delivered a prioritized, ROI-ranked AI use-case roadmap for the GTM function.