Code review skills
senior engineers need

Code review is how teams share knowledge, catch defects and keep systems coherent. For engineers moving into senior and tech lead roles, review quality is one of the clearest signals of judgement — what you notice, what you let go and how you say it. Google's public engineering practices describe the goal as improving the overall health of the codebase over time, not perfection in every change.¹

The skill is becoming more important. Stack Overflow's 2025 survey found most developers use or plan to use AI tools, and that their top frustration is code that's "almost right, but not quite".⁵ As more code is generated, careful review becomes the bottleneck — and the differentiator. Senior roles that rely on this judgement pay well: software architects in India typically earn ₹32.1–35.5 lakh.⁷

Developers using or planning to use AI tools (Stack Overflow, 2025)

84%

Developers frustrated by AI output that's "almost right" (Stack Overflow, 2025)

66%

A common upper limit for a first review response

1 day

The pull-request size that gets the best reviews

Small

Table: How code review skills are assessed (editorial summary)

1. Read the description first — Understand the goal before the diff.
2. Start with the big picture — Design and correctness before details.
3. Be specific and explain why — "This query runs per item; with 500 items that's 500 calls. Could we batch it?"
4. Separate blocking from optional — Label nits; don't block on preferences.
5. Ask, don't accuse — "What happens if the list is empty?" invites thinking.
6. Respond quickly — Google's guidance stresses speed because slow reviews block teams.²

Table: Weak vs strong code review comments (illustrative)

Quality shouldn't depend on one vigilant reviewer. Teams that ship reliably automate formatting and linting, run static analysis and tests in CI, require small pull requests, and track delivery outcomes — the kind of practices DORA's research links to better software delivery performance.³ Reviewing a few hundred lines at a time is widely recommended because reviewers' effectiveness falls on large changes.⁴

"What do you look for first in a review?"

Whether the change does what it should, safely: correctness, security and design — then tests and readability.

"How do you handle a disagreement in review?"

Clarify the concern, explain trade-offs, look for data or a quick spike, and if still stuck, involve a third person or follow team conventions — then move on.

"A teammate's PR is 3,000 lines. What do you do?"

Ask to split it, or review in a call with the author walking through it; suggest stacking smaller PRs in future.

"How do you review AI-generated code?"

The same as any code, with extra attention to plausible-looking mistakes: missing edge cases, incorrect API use, security gaps and licence concerns.

"How do you keep reviews fast?"

Small PRs, clear descriptions, automation for style, review slots in the day and responding to first review within hours.

"What is technical debt and how do you manage it?"

Shortcuts that make future change harder. Make it visible, pay it down alongside features, and stop adding it where it costs most.

Table: How code review has changed (editorial summary)

1. Review open source — Read reviews on a well-run open-source project; note what maintainers catch.
2. Rewrite your comments — Take five past comments and make them specific, kind and justified.
3. Self-review first — Review your own PR before requesting review, every time.
4. Automate — Set up formatting, linting and a test gate in CI for a project.
5. Review AI output — Generate a feature with an assistant and write a full review of it.

Table: Roles where code review matters and typical pay in India (AmbitionBox, October 2026; indicative)

1. Week 1: Read the guides — Google's engineering practices and OWASP's Code Review Guide.
2. Week 2: Practise — Review ten open-source PRs privately; compare with maintainers' comments.
3. Week 3: Improve your team's system — Add automation and a PR template.
4. Week 4: Prove it — Take the Code Review and Code Quality Certification.

Related: Secure Coding Fundamentals, AI-Assisted Software Development and Git and GitHub.

• Daily review slots — reviewing teammates' pull requests within hours, not days.
• Self-review — reading your own diff before asking anyone else.
• Design conversations — moving long review threads into a quick call and recording the decision.
• Mentoring — explaining the "why" behind comments for junior engineers.
• Quality tooling — maintaining linters, formatters and CI checks.
• AI-generated changes — reviewing them with the same rigour as human code.

• Nitpicking while missing real bugs.
• Harsh or vague comments.
• Slow reviews that block teammates.
• Rubber-stamping large PRs.
• Using review to gatekeep instead of teach.

Guidance draws on Google's public engineering practices (code review and review speed), DORA's research, SmartBear's peer-review best practices and OWASP's Code Review Guide; AI-use figures from Stack Overflow's 2025 survey; pay from AmbitionBox (read October 2026). Assessment settings and the history table are editorial summaries. Science of Apps publishes the free certification linked here. Last reviewed October 2026.

• Pull request — a proposed change submitted for review before merging.
• Nit — a minor, optional suggestion.
• Static analysis — automated checks of code without running it.
• Technical debt — design shortcuts that make future change harder.
• Change failure rate — the share of deployments that cause problems in production.