The Git Query That Finds Your New Hire's Perfect Mentor

Engineering onboarding is often a mix of intuition and tribal knowledge. New hires get paired with whoever's available, pointed at a wiki that's three years out of date, and left to figure things out. But your Git history contains a wealth of data that can make onboarding faster, more targeted, and more effective.

This guide shows you how to use Git activity data to identify the right mentors, focus onboarding on high-impact areas, and track how quickly new hires are ramping up.

Why Data-Driven Onboarding Works

The Problem with Traditional Onboarding

Most engineering onboarding suffers from common problems:

• Random mentor assignment: New hires get paired with whoever's free, not necessarily who knows the code they'll work on
• Generic curriculum: Everyone reads the same docs, regardless of their role or first project
• Outdated documentation: Wikis and READMEs don't reflect how the code actually works today
• No progress visibility: Managers can't tell if onboarding is working until the new hire either succeeds or struggles

What Git Data Tells You

Your Git history is a real-time map of your codebase:

• Who owns what: Commit history shows who has worked on each file and module most recently
• What changes most: High-churn files are where new hires will likely need to work
• Who reviews what: Review patterns show who's best positioned to mentor in each area
• Ramp-up patterns: Past new hire activity shows normal progression curves

Benefits of Data-Driven Onboarding

• Faster ramp-up: New hires learn from the right people about the right code
• Better mentor matches: Pairing based on actual expertise, not availability
• Targeted learning: Focus on high-impact areas, not the entire codebase
• Measurable progress: Track ramp-up with concrete metrics

Identifying Domain Experts for Pairing

Finding the Right Mentor

The ideal mentor for a new hire isn't just someone senior—it's someone who:

• Has deep knowledge of the code the new hire will work on
• Is actively working in that area (not someone who touched it 2 years ago)
• Has bandwidth to mentor (isn't already overloaded)
• Has good communication skills (subjective, but important)

Using Git Data to Find Experts

Analyze recent commit and review activity to identify domain experts:

But commits only show authorship. Review activity shows who understands the code well enough to review it:

Avoiding Single Points of Failure

Sometimes your "domain expert" is actually a knowledge silo—one person who's the only one who knows an area. This is a warning sign:

• For onboarding: If only one person knows an area, they might not have bandwidth to mentor
• For the team: Knowledge silos are bus factor risks
• Opportunity: Onboarding a new hire into a silo area helps spread knowledge

Focusing on High-Impact Areas

Not All Code Is Equal

New hires can't learn the entire codebase at once. Focus onboarding on:

• High-churn files: Code that changes frequently is where they'll work most
• Their team's domain: The specific modules their team owns
• Critical paths: Core business logic they'll need to understand

Identifying High-Churn Areas

Code hotspots—files that change frequently—are where new hires will spend most of their time:

Building a Learning Path

Combine high-churn data with domain ownership to create a targeted learning path:

Tracking Ramp-Up Progress

What Ramp-Up Looks Like in Data

A healthy onboarding progression shows up in Git activity:

• Week 1-2: Few commits, mostly documentation reads and environment setup
• Week 3-4: First small commits, high review feedback
• Month 2: Regular commits, feedback decreasing as code quality improves
• Month 3: Contributing like a team member, starting to review others' code

Metrics to Track

Monitor these metrics to gauge onboarding progress:

1. Commit Frequency

Track commits over time. Healthy pattern: starts slow, ramps up, stabilizes around team average by month 2-3.

2. PR Cycle Time

New hires typically have longer cycle times initially due to more review rounds. This should decrease:

3. Review Participation

A key milestone: when new hires start reviewing others' code. This signals they understand the codebase well enough to evaluate others' work.

4. First-Time Review Approval Rate

Track how often the new hire's PRs are approved on first review vs. needing changes. Early: low. Over time: should approach team average.

Setting Expectations

Share ramp-up expectations with both the new hire and their manager:

Building an Onboarding Checklist

Before Day 1

• Identify domain expert mentor using Git/review data
• Pull list of high-churn files in new hire's team domain
• Prepare first small task (bug fix in a hotspot file)
• Schedule mentor 1:1s for first two weeks

Week 1: Environment and Context

• Dev environment setup (documented, mentor assists)
• Codebase walkthrough focusing on high-churn areas
• First trivial commit (typo fix, small improvement)
• Daily check-ins with mentor

Week 2: First Contribution

• Assigned first real task (small bug or minor feature)
• First PR opened and reviewed
• Learn team's PR conventions and review culture
• Attend team ceremonies (standup, planning, retro)

Month 1: Building Momentum

• Complete 3-5 PRs of increasing complexity
• Deep dive into one module (with domain expert)
• Identify one area for documentation improvement
• Weekly mentor 1:1s continue

Month 2-3: Full Participation

• Take on feature-level work
• Start reviewing others' PRs
• Contribute to technical discussions
• Mentor check-ins become bi-weekly

Tracking Success

Create a simple dashboard to track each new hire's progress:

Data-driven onboarding takes the guesswork out of bringing new engineers up to speed. By using Git activity to match mentors, focus learning, and track progress, you can cut ramp-up time and set new hires up for success from day one.