Knowledge Architect and Product Enablement Expert

Docplanner

6d•Remote

About The Position

Docplanner Group is seeking a Knowledge Architect and Product Enablement Expert to define and manage the structure of product knowledge. This role involves creating and maintaining a content model for both internal and external knowledge bases, setting quality standards for factual accuracy and referencing, and ensuring consistency across different AI assistants (Kraken for customers and Noa for doctors). The expert will be responsible for extracting knowledge from code, improving generation processes, and simplifying content. They will also review and restructure the current internal knowledge base, proposing a prioritized plan for updates and leading the team through this transition. A key part of the role is teaching the team new skills, such as writing self-contained facts, reviewing generated content, and testing AI assistants. Additionally, the expert will identify knowledge not contained in code (e.g., market differences, UI wording), design processes for acquiring this information with cross-functional teams, and manage the review loop. Maintaining up-to-date knowledge with frequent releases, defining update triggers, and alerting relevant teams are also crucial. The role involves collaborating with Product Experts, the Help Center team, and the owners of Kraken and Noa, ensuring smooth handoffs and quality control. Engineering support for repository access and code changes related to knowledge extraction will also be required.

Requirements

4 to 7 years of experience in technical writing, documentation engineering, knowledge management, developer experience, solutions engineering, or a comparable field with demonstrated ownership.
Real comfort with Git and GitHub, including branches, pull requests, reading changes, navigating large codebases, and reading code sufficiently to understand rules and special cases.
Experience designing content structures, including small self-contained content pieces, labels, metadata, and defining standards for others to follow.
Daily use of Markdown, YAML, or JSON as formats.
Experience with documentation that lives in a repository (versioned, reviewed, and built like code).
A working understanding of how AI assistants use content, including retrieval mechanisms, reasons for bad answers from well-written articles, and methods for testing answers.
Proven ability to get busy experts to review facts quickly and design formats that facilitate this.
Experience teaching a team a new way of working and ensuring adoption through guides, examples, and sessions.
Independence to build a role that has no precedent, defining its scope, limits, and proactively raising risks.
Professional English, written and spoken.

Nice To Haves

Any additional language is a plus (Spanish, Portuguese, Polish, Italian...).

Responsibilities

Define and maintain the content model for Docplanner product knowledge, including fact structure, organization, labels, metadata, and internal/external knowledge base delineation.
Set quality standards for knowledge content, ensuring every fact has a reference to its source.
Design a unified structure for knowledge that works for both customer-facing (Kraken) and doctor-facing (Noa) AI assistants.
Extract knowledge directly from code in GitHub repositories, understanding feature logic, rules, and special cases to create clear, reviewable output.
Improve the automated generation of knowledge output from code to minimize manual intervention.
Ensure all new or changed features are generated from code from day one.
Review the current internal knowledge base article by article, deciding on its fate (move, rewrite, replace with generated output, or archive).
Propose a prioritized order for knowledge base restructuring based on usage and change frequency, and gain agreement from the team manager.
Lead the team through the knowledge base restructuring process, defining methods, explaining them, reviewing results, and ensuring consistent quality across markets and people.
Teach the team to transition from writing articles to checking facts, creating guides, examples, and training sessions.
Build new team skills in writing self-contained facts, reviewing generated content, spotting knowledge gaps, working with repository content, and testing AI assistant answers.
Act as the primary point of contact for the team's knowledge-related questions and document answers for future reference.
Identify types of knowledge not contained in code (e.g., market differences, UI wording, patient-facing elements).
Design repeatable processes for acquiring non-code knowledge in collaboration with Product Managers, Product Marketing, and Product Experts.
Run review loops with feature owners for non-code knowledge.
Prepare facts in a format easily confirmable, correctable, or rejectable by non-technical personnel.
Track and analyze returned feedback on non-code knowledge to identify recurring issues.
Define processes for keeping knowledge up-to-date with frequent releases, including triggers, alerts, and commitment times for changes.
Alert the Help Center team about release-induced inaccuracies in published content, providing precise information for fixes.
Facilitate input from Product Experts into the internal knowledge base, shifting their role to fact-checking rather than writing.
Define the format of handed-over output and alerts for the Help Center team, who will own publishing.
Collaborate with owners of Kraken and Noa to define received output and test assistant answer quality against a fixed question list.
Liaise with engineering for repository access, monorepo migration plans, and necessary code changes for knowledge extraction.