CX Knowledge Architect
Fixed Frames · New York, NY · 2 days ago
On-siteArt & Creative$136k–$160k/yrFull-time
About the role
Own CX enablement knowledge strategy + systems globally. Notion ships quickly, and our customer experience depends on support teams being able to find accurate answers fast. This role exists to keep our internal knowledge base fresh as it scales—by designing durable information architecture and governance that keeps pace with frequent product change.
What's different at Notion
You’ll use Notion as customer zero, building a living system of record structured for both humans and AI—so the right people (and the right agents) can retrieve the right information at the right time.
What You'll Achieve
- Own end-to-end CX knowledge as a system: Make it dramatically faster for CX teams to retrieve trustworthy, up-to-date knowledge, improving support quality and efficiency as Notion grows.
- Design and evolve information architecture (IA): Define how knowledge is structured, governed, maintained, and measured across the full lifecycle (intake → draft → review → publish → maintenance → archive).
- Set quality standards + governance: define templates, authoring guidelines, and review/approval paths (especially for high-risk policy areas like billing, legal, security, pricing).
- Maintain accuracy + freshness at scale: expand knowledge creation + maintenance programs so launch content and updates don’t bottleneck on a single team.
- Build modular, reusable knowledge: drive standard sections and reusable modules (definitions, constraints, escalation paths, policy snippets) using synced blocks and consistent structure to enable reuse across KB + macros.
- Supervise content agents + KB automation: Optimize the KB for AI retrieval, and use AI to improve quality and coverage (AI-assisted content QA, gap detection, and draft suggestions with human review), with clear standards for what gets published.
- Partner cross-functionally for support readiness: triage and coordinate requests from CX, Product, Eng, and Legal/Finance; translate decisions into support-ready guidance (decision trees, escalation paths, KB updates, internal comms).
- Measurably improve freshness, launch coverage, and workflow efficiency—while raising the bar on the system’s quality, usability, and durability.
Skills You'll Need To Bring
- Program + system ownership: experience owning an end-to-end knowledge system (not just shipping individual docs) in knowledge management, CX enablement, technical writing, content ops, or adjacent roles.
- Information architecture + governance depth: ability to design scalable IA (taxonomy/tagging/page structures) and run governance/review models that protect quality and reduce risk.
- Systems thinking + operational excellence: can build durable processes that scale across many contributors and withstand high change velocity; experience building modular, reusable content components.
- Quality and maintenance rigor: demonstrated ability to run content QA, audit cadences, and freshness programs.
- Data-informed prioritization + AI literacy: uses qualitative + quantitative signals to focus on the highest-impact work, and understands how structure affects AI/human retrieval.
Qualifications
- To bring program + system ownership, you should have experience owning an end-to-end knowledge system (not just shipping individual docs) in knowledge management, CX enablement, technical writing, content ops, or adjacent roles.
- To bring information architecture + governance depth, you should have ability to design scalable IA (taxonomy/tagging/page structures) and run governance/review models that protect quality and reduce risk.
- To bring systems thinking + operational excellence, you should have experience building durable processes that scale across many contributors and withstand high change velocity; experience building modular, reusable content components.
- To bring quality and maintenance rigor, you should have demonstrated ability to run content QA, audit cadences, and freshness programs.
- To bring data-informed prioritization + AI literacy, you should use qualitative + quantitative signals to focus on the highest-impact work, and understand how structure affects AI/human retrieval.