Roles & Capability Paths: Starter Catalog
Seed role profiles, two-level capability matrices, interview questions and a hiring scorecard, and practical 30/60/90 ramp plans plus a 90-day success checklist for Knowledge Managers, Learning Engineers, Experiment Owners, and Analytics Partners. Includes governance patterns and tailoring notes so teams can adopt or adapt these templates to local context.
Purpose
This starter catalog helps organizations clarify who owns learning, knowledge work, and capability development. It provides pragmatic role profiles, a two-level capability matrix (foundation, advanced) for each role, sample interview questions with a simple hiring scorecard, suggested 30/60/90 ramp plans, a 90-day success checklist for new hires, and lightweight governance patterns to reduce overlap and ensure reliable handoffs. Use these templates as a starting point — adapt competencies, metrics, and titles to match your organization.
Core roles (seed profiles)
Knowledge Manager (KM)
Primary outcome: Ensure organizational knowledge is captured, discoverable, maintained, and used to improve performance.
Key responsibilities- Design and maintain knowledge taxonomies, content lifecycle, and access controls.
- Run content curation, validation cycles, and knowledge-refresh cadences.
- Partner with teams to capture lessons, best practices, and decision records.
- Measure usage, findability, and content quality; drive continuous improvements.
- Search find rate or first-click success (%) for target queries.
- Time-to-onboard (average reduction attributable to knowledge content).
- Number of validated playbooks or decision records created per quarter.
- Content quality score from regular peer reviews.
Learning Engineer (LE)
Primary outcome: Design learning experiences that close skill gaps and increase on-the-job capability.
Key responsibilities- Turn capability maps into learning paths, microlearning, and assessments.
- Integrate performance support tools into flow-of-work systems.
- Measure learning impact and iterate on content and activities.
- Post-training performance improvement on core KPIs.
- Completion and competency attainment rates for learning paths.
- Reduction in errors or rework after training interventions.
Experiment Owner (EO)
Primary outcome: Run small, measurable experiments to test improvements and convert validated experiments into repeatable practices.
Key responsibilities- Design hypothesis-driven experiments with clear metrics and guardrails.
- Coordinate cross-functional test execution and collect results.
- Document findings, decisions, and recommended next steps (scale, iterate, stop).
- Proportion of experiments delivering actionable insight.
- Cycle time from hypothesis to validated result.
- Number of experiments that led to scaled process changes per period.
Analytics Partner (AP)
Primary outcome: Turn data into timely insights and decision-grade analysis that teams can act on.
Key responsibilities- Provide analytics design, tooling, dashboards, and ad hoc analysis support.
- Work with domain owners to define meaningful KPIs and data definitions.
- Operationalize analysis into repeatable reports and decision triggers.
- Time-to-deliver standard reports and ad hoc requests.
- Adoption and utilization rates of dashboards.
- Correlation between analytics-led recommendations and improved outcomes.
Two-level capability matrix (Foundation / Advanced)
For each role, evaluate people across two levels. Use these as observable behaviors rather than checkboxes.
Knowledge Manager
- Foundation: Creates clear content templates; enforces naming conventions; manages a content backlog; runs quarterly content reviews.
- Advanced: Designs taxonomy strategy tied to outcomes; automates lifecycle workflows; runs cross-site knowledge governance and metrics reporting.
Learning Engineer
- Foundation: Builds effective microlearning and assessments; measures completion and satisfaction; incorporates feedback loops.
- Advanced: Designs adaptive learning paths integrated with performance systems; demonstrates measurable ROI on learning interventions.
Experiment Owner
- Foundation: Designs valid A/B or small-sample tests; tracks basic metrics and documents outcomes.
- Advanced: Runs rigorous causal analysis, scales winning experiments, and embeds learnings into process standards.
Analytics Partner
- Foundation: Delivers accurate data extracts, dashboards, and routine reports; understands key data definitions.
- Advanced: Builds predictive models or decision-support tools; creates self-serve analytics and trains domain teams to use them.
Sample interview questions & scoring guidance
Use a consistent 1–5 rubric (1 = poor, 5 = excellent). Evaluate evidence, not claims.
Knowledge Manager (examples)
- Describe a time you converted tacit team knowledge into a usable process or playbook. (Assess structure, stakeholder engagement, and outcome.)
- How do you decide when to archive or retire content?
Learning Engineer (examples)
- Show an example of a learning experience you designed that improved on-the-job performance. How did you measure impact?
- How do you decide when to use microlearning versus longer-form training?
Experiment Owner (examples)
- Give an example of an experiment you ran. What was the hypothesis, result, and action taken?
Analytics Partner (examples)
- Explain a complex analysis you produced for a non-technical audience. How did you ensure it was actionable?
Hiring scorecard (template): For each question or competency, capture a 1–5 score and a short justification. Weight core competencies (role-fit, communication, delivery) higher than secondary skills. Example columns: Competency, Weight (0–1), Score (1–5), Weighted score = Weight × Score. Sum weighted scores and define cutoffs for hire / consider / decline based on your staffing needs.
Suggested 30/60/90 ramp plan (template)
Customize details per role and site. Use these headings as observable milestones.
- First 30 days: Meet stakeholders, learn systems, review existing knowledge assets, complete onboarding learning modules, and deliver a short discovery report of quick wins and knowledge gaps.
- 30–60 days: Own a small initiative (e.g., publish a playbook, run a learning pilot, execute an experiment, deliver a dashboard). Establish regular reporting cadence with manager.
- 60–90 days: Demonstrate measurable impact from the initiative, propose next quarter roadmap, and onboard at least one stakeholder to co-own ongoing work.
90-day success checklist (example)
- Introduced to and met with all core stakeholders.
- Completed required systems and security access.
- Delivered a documented discovery with prioritized opportunities.
- Published or updated at least one knowledge asset or learning module.
- Ran or planned an experiment or analytics deliverable with clear metrics.
- Received initial feedback from stakeholders and set a 6-month roadmap.
Governance pattern (simple)
Assign clear ownership using RACI-like rules for knowledge artifacts: Owner (maintains), Steward (ensures quality), Contributor (provides domain input), Consumer (uses and requests changes). Create an approval loop for published playbooks and a quarterly review cadence. Keep role responsibilities lightweight to avoid bureaucracy: focus on observable outcomes and delivery commitments rather than lengthy job descriptions.
Tailoring guidance
These templates are intentionally generic. Before adopting, work with hiring managers to: align language to local titles, map local KPIs to success metrics, set realistic competency thresholds based on staffing supply, and identify which artifacts (playbooks, dashboards, curricula) are highest priority.
Where to go next
Convert the hiring scorecard and 90-day checklist into an interactive form (so hiring panels store scores and onboarding progress). Consider packaging this catalog as an adaptive domain or toolkit that local sites can copy and tailor while preserving core governance patterns.
Discussion
Comments and conversation will live here.