Learning Ops & Knowledge Roles Catalog (Expanded Role Cards)

Purpose

This concise catalog turns common learning-ops roles into usable role cards you can adapt for hiring, onboarding, career ladders, or governance. Each card focuses on core responsibilities, practical KPIs you can measure, hiring signals that matter in practice, and typical training or career pathways. Use these as starting points — tailor language, scope, and level expectations to your team size, industry, and risk profile.

How to use these cards

• Copy a card into a job posting or internal role description and adjust scope (individual contributor vs. team lead) and domain (clinical, manufacturing, customer support, R&D).
• Pick 3–5 KPIs from the suggested list and align them to your quarter or year goals; keep them outcome-focused and evidence-based.
• Use the hiring signals as interview probes and the training pathways as onboarding milestones.
• Do not treat title or a checklist as proof of competence — require evidence of results, examples of past work, or a small hiring experiment when possible.

Role Cards

Knowledge Manager

Primary responsibility: Own the lifecycle of institutional knowledge — capture critical processes and lessons, keep documentation findable and current, and govern knowledge standards across teams.

• Shareability: % of high-impact processes with current, versioned documentation.
• Findability: Average time to find key SOP or onboarding guide.
• Adoption: % of teams using the canonical knowledge repository for routine tasks.
• Knowledge freshness: % of documents reviewed/updated within scheduled review window.

• Experience building or running a content/knowledge repository (wiki, CMS, intranet) with governance rules.
• Shows examples of turning tacit team knowledge into practical documentation or playbooks.
• Understands information architecture and tagging/taxonomy basics.

• Library/information science, technical writing, or operational excellence exposure.
• On-the-job projects converting tribal knowledge to standard work.

Learning Engineer

Primary responsibility: Design and deliver learning solutions that close measurable performance gaps — from micro-learning and curricula to integrated learning experiences that embed into workflows.

• Learning impact: % improvement on a defined performance metric after intervention.
• Completion and application: % of learners completing key modules and applying skills in the next 30–90 days.
• Time-to-competence: Average time for new hires to reach defined competency levels.

• Portfolio of learning artifacts tied to measurable outcomes (e.g., reduced error rates, faster onboarding).
• Comfort with rapid prototyping of learning, lightweight experiments, and evaluation design.

• Instructional design, UX for learning, adult learning theory, plus domain experience.
• Hands-on experience collaborating with SMEs and deploying small pilots.

Experimentation Lead

Primary responsibility: Run structured experiments and improvement cycles (A/B tests, pilots, PDSA) that answer high-value questions and reduce risk for scaling changes.

• Experiment velocity: Number of well-scoped experiments run per quarter.
• Decision yield: % of experiments that produce a clear, documented decision (adopt, adapt, abandon).
• Value realized: Estimated impact from adopted experiments (e.g., cost savings, quality improvement).

• Track record running experiments with clear hypotheses, metrics, and learnings.
• Comfort translating business questions into testable designs and lightweight analytics.

• Background in product management, operations, quality, or behavioral science with experience in experimentation methods.

Analytics Translator

Primary responsibility: Bridge data, analytics, and operational teams — frame questions, interpret models, and turn insights into actionable recommendations for non-technical stakeholders.

• Insight adoption: % of analytics insights that lead to operational changes tracked to outcomes.
• Time-to-decision: Average time from question to actionable insight delivery.
• Clarity: Stakeholder satisfaction score on analytics deliverables.

• Can explain a technical model or dataset in plain language and propose clear next steps.
• Experience translating requirements between business users and data teams.

• Data analytics, business intelligence, or domain experience combined with communication skills.

Community Facilitator

Primary responsibility: Grow and steward communities of practice or internal forums that accelerate peer learning, best-practice sharing, and collective problem solving.

• Engagement: Active participation rate in community channels or events.
• Knowledge flow: Number of actionable contributions (solutions, playbooks) produced by the community.
• Network effect: % of requests resolved via community vs. central support.

• Experience running groups, facilitating discussions, and creating rituals that sustain participation.
• Strong empathy and a bias toward enabling others rather than owning every answer.

• Community management, facilitation training, adult learning or field experience in the practice area.

Staffing & Governance Patterns

• Centralized vs Embedded: Small orgs often start with a central Knowledge Manager + cross-functional Learning Engineer(s). Larger orgs benefit from central standards plus embedded learning ops specialists in high-risk or high-growth units.
• Ownership clarity: Assign a single owner for each knowledge domain and a clear escalation path to avoid duplicated effort.
• Career ladders: Define competency matrices (skills, impact, leadership) rather than rigid titles so individuals can grow horizontally (specialist) or vertically (manager).

Quick Hiring Checklist

1. Define the outcome you expect this role to deliver in 90 and 365 days.
2. Pick 3 measurable KPIs from the card and require examples of past work that map to one or more KPIs.
3. Give a short paid or unpaid trial assignment (e.g., draft a 1-page playbook or run a 2-week pilot) where feasible.
4. Plan onboarding milestones that map to the typical training pathways listed above.

Adaptation note

These cards are intentionally adaptable. Avoid copying them verbatim. Translate language into your organization's terms, simplify KPIs for operational teams, and combine or split roles depending on maturity, budget, and risk. Use small experiments to prove role impact before scaling staffing.