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DocPlanner Group is the world's largest healthcare platform, connecting 24 million patients with 280k doctors across 13 countries through brands like ZnanyLekarz, Doctoralia, MioDottore, DoktorTakvimi, and jameda. The company operates marketplaces, SaaS, and AI tools that help doctors, clinics, and hospitals work more efficiently.
You will own the complete architecture of DocPlanner's product knowledge system, serving as the bridge between technical implementation and user-facing documentation. This is a foundational role with no precedent in the company—you will define its scope, defend its boundaries, and shape how knowledge flows across the organization.
Key responsibilities include:
**Structuring Product Knowledge**: Define and maintain the content model for all DocPlanner product knowledge—from self-contained facts to topic organization, labeling, metadata standards, and the distinction between internal and external knowledge bases. Every fact must carry a reference to its source so it can be verified rather than blindly trusted.
**Extracting Knowledge from Code**: Work directly in GitHub with read access to the monorepo and other repositories. Read feature logic well enough to understand rules and edge cases, then translate that into clear, reviewable output. Continuously improve the generation process so fewer facts require manual confirmation over time. Enforce the rule that every new or changed feature generates knowledge from code from day one.
**Auditing and Restructuring Existing Knowledge**: Review the current internal knowledge base article by article, deciding what to move to the new structure, rewrite, replace with code-generated content, or archive. Propose a prioritized work plan based on usage frequency and change velocity, then lead the team through execution while maintaining quality consistency across markets.
**Teaching New Workflows**: Transition the team from writing articles to verifying facts—a significant behavioral shift. Create working guides, examples, and training sessions. Build skills in writing self-contained facts, reviewing generated content, spotting knowledge gaps, working with repository-based content, and testing AI assistant answers. Become the go-to resource when the team is uncertain, then document those answers for future reference.
**Handling Non-Code Knowledge**: Identify knowledge types the code doesn't contain (market differences, UI wording, patient-facing content, screenshots). Design repeatable processes for capturing this information with Product Managers, Product Marketing, and Product Experts. Prepare facts in formats non-technical stakeholders can quickly confirm, correct, or reject. Track what comes back wrong and why.
**Keeping Knowledge Current**: Define how knowledge stays current with dozens of monthly releases—what triggers updates, who gets alerted, and response time commitments. Alert the Help Center team when releases invalidate published content, with enough precision that they can fix it without re-reading entire articles.
**Cross-Functional Collaboration**: Work with Product Experts (who feed, test, and improve the knowledge base), the Help Center team (who publish customer-facing content), the owners of Kraken and Noa AI assistants (who consume your output), and Engineering (for repository access and code changes needed to support knowledge extraction).
**Requirements**
- 4 to 7 years of experience in technical writing, documentation engineering, knowledge management, developer experience, solutions engineering, or comparable field, with real ownership. This is not an entry-level role.
- Genuine comfort with Git and GitHub: branches, pull requests, reading diffs, navigating large unfamiliar codebases, and reading code well enough to understand rules and special cases. You do not need to be a software engineer, but you must be able to open a repository independently.
- Experience designing content structures: small self-contained pieces, labels, metadata, and standards you defined and enforced. Daily familiarity with Markdown, YAML, or JSON.
- Experience with documentation that lives in repositories: versioned, reviewed, and built like code—not stored in document folders.
- Working understanding of how AI assistants use content: retrieval mechanisms, why well-written articles can still produce poor answers, and how to test answers rather than assume quality.
- Proven ability to get busy experts to review facts quickly and design formats that make this possible.
- Experience teaching teams new workflows and making them stick through guides, examples, sessions, and patience.
- Independence to build a role with no precedent: define it, defend its scope, and raise risks before they become blockers.
- Professional English (written and spoken). Additional languages a plus: Spanish, Portuguese, Polish, Italian.
