Skill Graph Examples

The best way to understand how a skill graph works is to see real structures. This guide presents example graphs for five different roles, showing how domain groupings, skill nodes, and levels come together in practice.

Use these examples as starting templates. Copy the structure that is closest to your role, replace the nodes with your own skills, and adjust the levels based on your experience.

Example 1: Full-Stack Engineer

A full-stack engineer's graph balances frontend, backend, and delivery skills. The most common gap at senior levels is infrastructure and observability.

Domain Structure

Key Edges

• React → TypeScript (co-dependency — used together daily)
• API design → Database modelling (prerequisite — you need to understand data shape before designing endpoints)
• Incident response → Observability (adjacent — incident handling improves when monitoring is strong)

Common Gaps

• Observability: Many full-stack engineers ship features but never configure alerting or structured logging. This blocks the path to staff engineer.
• Accessibility: Often neglected but increasingly required for enterprise clients and regulatory compliance.
• Technical writing: The gap between "senior" and "staff" is often the ability to write clear RFCs and architecture proposals.

Example 2: Product Manager

Product managers need a broad graph that spans discovery, strategy, execution, and leadership. The balance between depth and breadth matters more here than in engineering roles.

Domain Structure

Key Edges

• User research → Opportunity sizing (prerequisite — research informs sizing decisions)
• Experiment design → A/B testing (co-dependency)
• Stakeholder management → Executive communication (skill family)

Common Gaps

• Data literacy: Many PMs can define metrics but struggle with SQL or experiment interpretation. This is the most common gap blocking the jump from PM to Senior PM.
• Pricing strategy: Rarely taught but critical for B2B product managers at growth-stage companies.

Example 3: Data Scientist

Data scientists operate at the intersection of statistics, engineering, and domain expertise. The graph reflects this three-pillar structure.

Domain Structure

Key Edges

• Hypothesis testing → A/B testing → Causal inference (progression chain)
• Python → Spark (adjacent — Spark extends Python-based workflows to distributed scale)
• Data storytelling → Dashboard design (adjacent)

Common Gaps

• MLOps and deployment: Many data scientists can build models but cannot deploy, monitor, or retrain them in production. This is the top gap separating "data scientist" from "ML engineer."
• Causal inference: Moving from correlation to causation is a skill gap that separates senior data scientists from mid-level ones.

Example 4: Marketing Lead

Marketing leaders need a graph that spans channel expertise, analytics, and people management. The balance shifts toward strategy and leadership at senior levels.

Domain Structure

Common Gaps

• Attribution modelling: Many marketers can run campaigns but cannot measure their true impact. This blocks the path to VP-level roles.
• Budget allocation: Understanding how to distribute spend across channels with different payback periods.

Example 5: Engineering Manager

Engineering managers straddle technical depth and people leadership. Their graph typically has the widest domain spread.

Domain Structure

Key Edges

• Code review → Coaching (transfer — the skill of giving constructive feedback on code transfers directly to giving career feedback)
• Project planning → Risk management (prerequisite)
• Hiring → Team topology (adjacent — who you hire depends on how you structure teams)

Common Gaps

• Conflict resolution: The single most avoided skill by new engineering managers, and the one that matters most.
• Capacity planning: Understanding how to model team throughput against roadmap commitments.

How to Adapt These Examples

1. Pick the example closest to your role and copy its domain structure.
2. Replace the skills with your own. Keep 15–30 nodes total.
3. Score levels honestly using evidence from recent work (last 6–12 months).
4. Identify 2–3 gaps that matter most for your next career milestone.
5. Build a 30-day plan to close those gaps — see the How to Build a Skill Graph guide for the planning framework.

FAQ

Can I combine examples if my role spans multiple areas?

Yes. Many roles — especially in startups — span multiple domains. Take the relevant domains from each example and combine them. Just keep the total node count under 30–40 to stay manageable.

What if my role is not listed here?

Use the example that is closest and adapt it. The domain grouping pattern (3–6 domains, 5–8 skills per domain) works for any role. The specific nodes change, but the structure stays the same.

How detailed should each skill node be?

Start broad ("Frontend") and add specificity only where it helps you make decisions ("React," "CSS architecture," "Accessibility"). If two skills always move together, keep them as one node. If they develop independently, split them.