Avoiding knowledge silos in growing engineering teams

Source: Dev.to

The Hidden Risks of Scaling an Engineering Team

When an engineering team expands from 5 to 15 members, the informal communication patterns that once worked flawlessly begin to deteriorate.

• Pull requests that previously received three thoughtful comments now get a single, rapid approval from someone lacking deep context.
• Questions that were once answered by anyone on the team are now routed to a single individual.

These changes signal that knowledge is concentrating in a few people, creating hidden risks for growing teams.

Why Specialization Isn’t the Issue

• It’s beneficial to have a team member who deeply understands the payment‑processing service.
• The problem arises when that person is the only one with that expertise.

Consequences of Knowledge Silos

Takeaway

To sustain growth, teams must distribute knowledge and formalize communication before silos become entrenched. This prevents bottlenecks, safeguards against unexpected absences, and maintains a high standard of code quality.

The Real Cost of Knowledge Silos

When critical knowledge is stuck in one or two people’s heads, the rest of the team starts to work around them. This creates bottlenecks that are often hard to spot on a project plan but are felt every day in the development cycle.

Bottlenecks and Single Points of Failure

You can spot this problem in your daily workflow:

• Pull‑request delays – Is there a specific part of the system where PRs always take days longer to get reviewed?
• Commit concentration – Is there a developer whose name appears on 90 % of the commits for a critical service? When that person gets sick or takes a week off, all related work grinds to a halt.

This isn’t just a resourcing issue; it’s a system‑design flaw in your team’s knowledge architecture. The team loses its ability to respond to incidents or ship features in that area, making planning and forecasting unreliable.

Why Everyone’s Learning Slows Down

In a siloed environment, developers without specific expertise tend to stay in their lane. They avoid picking up tasks in unfamiliar parts of the code because they know the review process will be slow and painful, blocked by the one person with all the context. This discourages curiosity and exploration.

Over time, this reinforces avoidance:

• No one feels comfortable touching or suggesting improvements to code that “isn’t theirs.”
• The team’s learning stalls, even as new people join.

Onboarding Takes Longer and Agility Suffers

New hires have a much harder time getting up to speed when critical context lives in a few individuals’ heads. They can’t simply read the code or docs to understand why a system was built a certain way; they must find the right person and hope they have time to explain it.

Consequences

• Longer ramp‑up time for new team members.
• Reduced agility – you can’t easily swarm on a critical project or reassign people to new priorities because the overhead of knowledge transfer is too high.

Bottom line: Breaking down knowledge silos restores flow, speeds up learning, and makes the team more resilient and adaptable.

Knowledge Distribution Needs to Be an Active Process

We often assume knowledge spreads naturally, just by working near each other. But as teams grow and codebases become more complex, the opposite happens: knowledge becomes concentrated in a single person. Without explicit practices to circulate it, you end up with a few overloaded experts and many hidden dependencies.

Why Documentation Alone Is Never Enough

• Docs become stale quickly.
• They often miss the “why” behind technical decisions.
• The most valuable knowledge is the context shared during design reviews or debugging sessions.
  • A wiki can tell you how a service works, but it can’t convey the three failed approaches that were tried before landing on the current solution.

Relying solely on documentation creates a false sense of security while tribal knowledge becomes more entrenched.

From “Who Knows What?” to “How Do We Learn Together?”

The goal is to shift the team’s operating model:

1. Stop treating individuals as the sole index of expertise.
2. Build a system where learning is continuous and expected.
3. Replace the question “Who do I ask about the auth service?” with “What’s the process for me to learn what I need to know about the auth service?”

This approach makes the whole team more resilient and capable.

Strategies for Building a Resilient Knowledge Culture

Breaking down silos requires intentional, consistent effort. There isn’t a single solution—rather, a combination of practices that reinforce each other. The goal is to make context‑sharing a natural part of the development workflow.

1. Distributing Knowledge Through Pairing and Rotations

Structured Pair Programming

• Pair an expert with a novice on a specific feature or bug within the expert’s domain.
• The explicit goal is knowledge transfer, not just faster ticket closure.
• Treat pairing as a scheduled, goal‑oriented activity (avoid making it optional or ad‑hoc).

Short‑Term Rotations

• Full‑team rotations are often too disruptive; instead rotate responsibilities:
  • On‑call rotation – a different developer each week handles operational aspects of a service they don’t normally own.
  • Bug‑czar rotation – assign a developer each sprint to triage and fix bugs for a particular component.

2. Creating Spaces for the Team to Share Knowledge

Tech Talks & Brown‑Bag Sessions

• Great for high‑level overviews, but the real value lies in the Q&A.
• Deep‑dive presentations surface undocumented gaps that become learning moments.

Dedicated Channels

• Set up Slack (or similar) channels for specific domains, e.g.:
  • #perf‑geeks
  • #frontend‑guild
  • #security‑champions
• These channels let specialists share articles, answer questions, and make expertise visible to the whole team.

Architectural Decision Records (ADRs)

• An ADR is more than a static document; its creation process drives learning.
• Writing an ADR forces you to articulate trade‑offs and alternatives.
• The PR for a new ADR serves as a team‑wide learning opportunity, sparking debate and clarifying the “why” behind decisions.

3. Mentorship Programs Within Engineering Teams

• Make mentoring intentional: senior engineers stay close to real contributions, offering code reviews, design feedback, and sharing undocumented context.
• Move beyond “point‑and‑show” to continuous, hands‑on guidance throughout a project’s lifecycle.

4. Empowering Developers to “Teach Back”

• Encourage developers who have recently mastered a system to lead a brown‑bag session or write a “getting started” guide for that component.
• Teaching reinforces the learner’s own understanding and produces beginner‑friendly documentation that experts often overlook.

By weaving these practices into daily routines, teams can build a resilient knowledge culture where information flows freely, expertise is distributed, and silos gradually dissolve.

Making These Practices Stick

Introducing these practices is one thing; making them a durable part of your team’s culture is another. It requires buy‑in from leadership and a willingness to adapt your approach over time.

Leadership’s Role in Prioritizing This Work

If leadership only measures feature velocity, any time spent on pairing, documenting, or mentoring will be seen as a cost. Engineering managers and tech leads need to actively champion these activities:

• Allocate dedicated time for them in sprint planning.
• Recognize and reward people for spreading knowledge, not just for shipping code.

How to Measure If It’s Working

Measuring knowledge sharing isn’t simple, but you can spot a few useful signals over time.

• Bus Factor – How many people on the team can safely handle a P1 incident on each critical service?

  • If the answer is “just one,” there’s a problem. Track this number regularly and aim for growth.

• PR Review Distribution – Examine who reviews and approves pull requests across the codebase.

  • Are PRs for the authentication service always approved by the same person?
  • A healthier team shows a broader distribution of reviewers.

• Onboarding Time – How long does it take a new engineer to deliver their first relevant feature without direct help?

  • When knowledge is more accessible, this time should shrink.

Iterate and Adjust

There’s no one‑size‑fits‑all set of practices. The key is to test, observe, and adjust:

• Formal pair programming may be too heavy for some teams.
• Collaborative reviews for complex PRs might work well.

Treat knowledge sharing as a real problem, keep experimenting, and evolve the solutions that fit your team’s workflow and culture.