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.
