Documentation Engineer / Knowledge Management Lead

The structure of our product knowledge:

• Define and keep the content model for Docplanner product knowledge, including small self-contained facts, topic organization, labels, metadata, and distinctions between internal knowledge bases and external customer-facing content.

• Set the quality standard for every fact, ensuring traceability to its source (repository, file, or code location) so it can be verified later.

• Choose a unified structure for both assistants (Kraken for customers and Noa for doctors), avoiding duplication of knowledge maintenance.

Getting knowledge out of the code:

• Work directly in GitHub with read access to the monorepo and other repositories. Find where the logic of a feature lives, read it thoroughly to understand rules and special cases, and translate it into clear, reviewable output.

• Improve the process of generating this output to reduce the need for manual confirmation over time. Simplification is key.

• Ensure every new or changed feature is generated from the code from day one. Older content is only revised when a release touches it or when it is one of the most used topics in Kraken and Customer Care.

Our current internal knowledge base:

• Review existing articles and decide their fate: move to the new structure, rewrite, replace with code-generated output, or archive if no longer useful.

• Propose a prioritized work order based on usage frequency and change frequency, with clear reasoning and agreement from the team manager.

• Lead the team through this transition, defining methods, reviewing results, and ensuring consistent quality across markets and team members.

Teaching the team to work in the new way:

• Transition the team from writing articles to verifying facts, requiring a shift in habits and thorough training.

• Create working guides, examples, and short training sessions to facilitate the new process and update them as needed.

• Develop new skills within the team, including writing self-contained facts, reviewing generated content, identifying knowledge gaps, working with repository-based content, and testing AI assistant answers.

• Serve as the team's go-to resource for questions, answering them and documenting answers to prevent future queries.

The facts that are not in the code:

• Identify types of knowledge not contained in the code, such as market-specific differences, screen wording, patient-facing content, and screenshots. Design repeatable methods to capture this information with Product Managers, Product Marketing, and Product Experts.

• Run a review loop with feature owners, preparing facts in a format easily confirmable, correctable, or rejected by non-technical reviewers. Track errors and reasons for them.

Keeping knowledge up to date:

• Define processes to keep knowledge current with frequent releases, including triggers for updates, alerting mechanisms, and response timelines.

• Alert the Help Center team when releases render published content incorrect, providing precise details for quick fixes without re-reading entire articles.

Working with the rest of the chain:

• Product Experts contribute, test, and improve the internal knowledge base. Ensure their role is focused on reviewing facts rather than writing them.

• The Help Center team publishes and maintains customer-facing content using your output. Ensure the format and alerts are clear and precise.

• Owners of Kraken and Noa use your output. Collaborate to define what they receive and test the quality of AI assistant answers against a fixed list of questions.

• Engineering provides repository access, monorepo migration plans, and any code changes needed to extract knowledge.