Engineering managers spend a lot of time on reviews. Yet, many engineers feel these reviews are unfair and discouraging. This shows a gap between old performance systems and the unique needs of engineering work. HR tools like Lattice and Workday track business goals well. But they often miss the special contributions engineers make.

Old systems focus too much on obvious results like new features. They ignore vital "glue work" like mentoring, improving processes, or detailed code reviews. They also have biases. For example, recent work can overshadow months of steady effort. Or one good project can shape the whole view of an engineer’s skills.

This guide helps engineering leaders build a fair, data-based performance system. You'll learn to use tools like GitHub, Jira, and Linear for better reviews. These steps save time and support career growth. For full automation, Exceeds AI can draft reviews from data in under 90 seconds.

Why Engineers Need a Better Performance System

Old performance systems often fail engineers and cost teams a lot. They miss key technical work like planning, reviews, or team collaboration. They value flashy results over quiet but critical "glue work" that keeps teams running smoothly.

These systems can be unfair due to bias. Reviews may rely on opinions, not facts. Managers might focus on recent work or let one big success color everything. This can frustrate engineers and push talent away.

Time is another issue. Managers spend hours on reviews. For larger teams, this adds up to hundreds of lost hours. Rushed reviews often lack useful feedback.

Unclear goals hurt the most. Old systems leave engineers unsure of their growth. Yearly reviews can feel like punishment, not help. This lowers motivation when teams need it most.

Engineering needs a system that fits how work happens. It should connect with tools like GitHub for code, Jira for planning, and docs for sharing. It must value impact over busywork, like a key design choice or mentorship talk over many small tasks.

Step 1: Define Success Beyond Just Writing Code

Start with a clear idea of what "success" means for engineers. It’s more than how much code they write.

Make a rubric that measures impact, not just output. Include technical skills like design and code quality. Add leadership traits like teamwork and planning. Also, value communication through good docs and discussions. Share this rubric with everyone.

Recognize "glue work" in your system. This means tasks like code reviews, onboarding, process fixes, and docs. Without this, you might reward solo efforts over team support.

Build a framework with clear areas of focus:

• Technical Skills: Code quality, design choices, and problem-solving.

• Results: Project success, less tech debt, and better reliability.

• Teamwork: Code review help, mentoring, and handling issues.

• Growth: Learning new skills and sharing knowledge.

Define levels for each area, from junior to senior roles. This clarity helps engineers see their career path. It also gives managers fair points to discuss performance.

Step 2: Use Data from Engineering Tools for Fair Reviews

Stop guessing and use data from tools engineers already use. Combine numbers from GitHub or Jira with feedback from docs and meetings. This builds a full picture of work.

Track different types of data to avoid focusing on just one thing. Look at pull request speed and deep code reviews. Check tech debt fixes and teamwork patterns. These show work quality without bias.

Don’t skip feedback that’s harder to measure. Track input on designs, mentoring, and issue fixes. GitHub shows review comments, while Jira highlights planning and support.

No single number tells the whole story. A senior engineer may write less code but have bigger impact through guidance. Gather data from many places over time for a fair view.

Focus on these data sources:

• GitHub/GitLab: Pull requests, review depth, and team contributions.

• Jira/Linear: Planning tasks, complex work, and team projects.

• Documentation: Writing guides, decisions, and onboarding help.

• Communication: Leading talks, mentoring, and issue response.

Use data over months to avoid focusing on recent work only. This balances reviews fairly across time.

Step 3: Automate Reviews to Save Time and Reduce Bias

Turning raw data into useful reviews takes time and can be unfair. Linking a code change to a design or a ticket to a team win is hard. Doing this by hand eats up hours for managers.

Automation makes this easier. Tools can connect data points, spot key work, and write fair reviews. They show both numbers and the story behind them, saving effort.

Exceeds AI helps with this by:

• Analyzing Work Data: Links GitHub, Jira, and other tools to show all contributions.

• Creating Drafts Fast: Turns months of data into reviews in under 90 seconds.

• Reducing Bias: Uses consistent rules and full data for fair results.

• Saving Hours: Cuts review prep time by up to 90%, freeing managers for coaching. One client saved over $100K in labor costs.

Automation fixes old review flaws. It looks at all work, not just recent tasks. It spots "glue work" managers miss. Most of all, it turns reviews into helpful growth talks.

Want fair, data-based reviews? Book a demo with Exceeds AI to save time and build trust in evaluations.

Step 4: Launch Your System Without Disruption

Roll out your new system step by step. Show its value fast while keeping risks low. This builds trust and protects current processes.

Try It with a Small Team First

Pick a group of 5-8 engineers for a test run. Include both junior and senior staff. Choose a team excited for better reviews and ready to give honest input.

In this test, check if your rubric works. Make sure data from tools shows real contributions. Confirm that "glue work" gets noticed and valued properly.

Improve Based on Feedback

Hold monthly check-ins during the test. Ask engineers and managers for detailed thoughts. Adjust metrics to keep things fair and clear.

Watch for odd results or gaming of numbers. If some work isn’t tracked well, update your data or rules. Fix issues early to keep the system honest.

Work with Current HR Tools

Don’t replace your HR systems like Workday or Lattice. Build your engineer system to fit with them. Send key data to HR tools while keeping detailed insights separate.

This mix gives deep engineer data and simple HR records. It avoids big changes or costs. Exceeds AI supports this by linking summaries to HR systems without replacing them.

Step 5: Help Managers Coach with Data

The goal of any system is better coaching for engineer growth. With solid data, managers can focus on helping team members improve and move forward.

Train managers to use data for growth talks, not just past judgments. Move from blame to "how can we help you grow?" This changes reviews into positive chats.

Key coaching skills include:

• Reading Data: Teach managers to value trends over raw numbers.

• Focusing on Growth: Use data to spot skill gaps and next steps.

• Two-Way Talk: Encourage engineers to share input on growth plans.

• Career Guidance: Link current work to clear future goals.

Exceeds AI boosts coaching by showing growth tips and suggesting mentors. It spots needs like design skills and matches engineers with team experts for help.

This creates a system focused on growth, not paperwork. Managers coach better, and engineers get clear steps to advance.

Frequently Asked Questions About Engineering Performance Systems

How Do You Track Hidden Work Like Mentoring?

Define mentoring and "glue work" in your rubric. Use tools to spot these tasks automatically. Look for deep code feedback, peer thanks, onboarding help, and discussion input. Exceeds AI tracks this in GitHub, Slack, and docs. Train managers to value and talk about teamwork in reviews.

Will Tracking Data Lead to Cheating?

This can happen, but good design stops it. Don’t focus on one number. Track many types of work like code, teamwork, and learning across tools. Look at patterns over time. Exceeds AI uses many signals for a full view that’s hard to trick. Regular team check-ins can spot and fix bad habits fast.

How Does This Fit with Tools Like Lattice or Workday?

New systems should work with your HR tools, not replace them. Use engineer tools for deep reviews, then send key info to HR systems. This keeps detailed data for teams and simple records for HR. Exceeds AI helps by linking summaries to HR platforms. It avoids big changes while improving engineer reviews.

How Can We Cut Bias in Reviews?

Reduce bias with data over full review periods, like quarters. Don’t rely on memory. Give equal weight to all work, not just recent tasks. Add peer and team feedback for balance. Use clear rubrics for fair judging. Automation helps by applying consistent rules to all data.

What’s the Value of a New Performance System?

You gain from saved time, better retention, and improved hiring. Cutting review time frees hundreds of hours for mid-size teams. Fair reviews keep top talent, saving $100,000+ per engineer in hiring costs. Teams feel more engaged with clear growth paths. Many see returns in one cycle from time savings alone.

Conclusion: Build a System That Helps Engineers Grow

A modern performance system for engineers moves from guesswork to data. Follow these five steps: define success, use tool data, automate reviews, launch carefully, and focus on coaching. This creates fair, fast reviews that support growth.

This change isn’t just about saving time, though hundreds of hours matter. It builds a culture where engineers feel valued and guided. When all work is seen, bias drops, and managers coach instead of doing paperwork, teams get stronger.

Leaders must decide: stick with unfair, slow reviews or adopt a system that helps retain talent and grow teams.

Ready for data-based reviews? Schedule a demo with Exceeds AI to save time and create trusted evaluations.

Sources

1. Your Performance Management Software Is Failing Your Engineers

2. Failed Performance Management: The Fix - Gallup.com

3. Why traditional performance management systems fail and how to fix them

4. Designing an Engineering Performance Management System from Scratch