Engineering Metrics Setup Checklist (2026)

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Git Analytics Foundation

Most engineering metrics start with git data. Set up collection before configuring specific metrics.

Connect all repositories to a git analytics toolcritical

Use Gitmore, LinearB, Sleuth, or similar to automatically collect metrics from your git platform. This is the foundation — without automated collection, metrics become a manual chore that gets abandoned.

Verify all team members are correctly attributedcritical

Ensure git email addresses map to the correct team members. Multiple email addresses, bot accounts, and shared accounts can skew metrics. Clean this up before trusting the data.

Configure team and repository groupingsimportant

Group repositories by team ownership so you can measure per-team metrics. In a monorepo, configure path-based ownership to attribute changes to the correct team.

Exclude automated PRs from metricsimportant

Dependabot, Renovate, and other automated PRs inflate throughput numbers. Configure your analytics tool to exclude them from human productivity metrics (or track them separately).

DORA Metrics

The four DORA metrics are the industry standard for measuring delivery performance.

Set up deployment frequency trackingcritical

Count production deployments per week from your CI/CD system or git tags. If you don't have automated deployments, count manual deployments. Baseline your current frequency before setting improvement targets.

Set up lead time for changes trackingcritical

Measure time from first commit to production deployment. This requires linking git commit data to deployment events. Most git analytics tools calculate this automatically if CI/CD is configured.

Set up change failure rate trackingimportant

Link incident data (from PagerDuty, Opsgenie, or your incident tool) to deployment data. CFR = incidents caused by deployments / total deployments. If you don't have an incident tool, start with a simple spreadsheet.

Set up MTTR trackingimportant

Track incident duration from detection to resolution. Pull timestamps from your incident management tool. Calculate median monthly MTTR.

Benchmark against DORA tiersimportant

Once you have baseline data, map each metric to its DORA tier (Elite/High/Medium/Low). This gives you a clear target: move each metric up one tier. Don't try to go from Low to Elite in one quarter.

Operational Metrics

Day-to-day metrics that help the team spot and fix bottlenecks quickly.

Track PR throughput per team per weekimportant

Number of PRs merged per team per week. Normalize per developer for teams of different sizes. Use this to spot throughput drops early.

Track code review turnaround timeimportant

Median time from PR creation to first review. Target: under 4 hours. This is the single most impactful operational metric for most teams.

Track cycle time per work typeimportant

Measure cycle time separately for features, bugs, and tech debt. Bug cycle time should be shorter than feature cycle time. If tech debt cycle time is very high, tech debt isn't being prioritized.

Track average PR sizenice-to-have

Median lines changed per PR. Target: under 200 lines. If PRs are consistently large, work isn't being broken into small enough increments.

Developer Satisfaction

Quantitative metrics miss the human side. Add developer satisfaction to complete the picture.

Run a quarterly developer satisfaction surveyimportant

Even 5 questions is valuable: overall satisfaction (1-10), biggest frustration, biggest win, tool satisfaction, and one open-ended question. Use DX, Google Forms, or Typeform.

Track survey results over timeimportant

A single survey is a snapshot. Tracking quarterly results reveals trends: is satisfaction improving, declining, or flat? Correlate satisfaction changes with organizational changes (new tools, team restructuring, workload shifts).

Act on survey results visiblyimportant

Share survey results with the team and commit to addressing the top 1-2 concerns. If developers give feedback and nothing changes, they stop participating. Visible follow-through is what makes surveys valuable.

Reporting & Review Cadence

Metrics are only useful if they're reviewed regularly and lead to action.

Set up a weekly automated metrics reportimportant

Configure Gitmore or your analytics tool to send a weekly summary to the team channel: PRs merged, review turnaround, deployments, and any anomalies. Automated reports ensure consistency without manual effort.

Review metrics in sprint retrospectivesimportant

Include a metrics snapshot in every retro: velocity, cycle time, review turnaround, and deployments. Ground retrospective discussions in data, not just feelings.

Create a monthly engineering dashboard for leadershipimportant

Summarize DORA metrics, quality indicators, and team health for your manager and cross-functional partners. Use the Engineering KPI Dashboard template for a ready-made format.

Set improvement targets quarterlynice-to-have

Based on current baselines, set one or two metric improvement targets per quarter. Example: 'Reduce median review turnaround from 18 hours to 8 hours.' Specific targets drive specific actions.

Pro Tips

Expert advice

1

Start with 5 metrics: deployment frequency, lead time, PR throughput, review turnaround, and developer satisfaction. Add more only when these are stable

2

Automate collection from day one — manual metric tracking gets abandoned within 2 months

3

Use Gitmore for automated weekly reports delivered to Slack — zero manual effort, consistent visibility

4

Never use metrics to rank individual developers. Use them at team level for process improvement

5

Baseline first, target second. Measure for 4-6 weeks before setting improvement goals — you need to understand normal variation

FAQ

Common questions

Which metrics tool should we use?

For git-derived metrics (PR throughput, review turnaround, cycle time, deployment frequency), use Gitmore — it connects to GitHub, GitLab, and Bitbucket and delivers automated reports to Slack. For incident tracking, use your existing incident management tool (PagerDuty, Opsgenie). For satisfaction, any survey tool works.

How long until we see meaningful data?

You'll see useful weekly operational data (PR throughput, review turnaround) within 1-2 weeks of setup. Monthly trend data (cycle time, deployment frequency) becomes meaningful after 4-6 weeks. DORA tier benchmarking requires at least one quarter of data.

What if the team resists metrics?

Resistance usually comes from fear of surveillance or being judged by numbers. Address it directly: metrics are for team improvement, not individual evaluation. Show the team the dashboard, let them discuss which metrics matter, and give them ownership of the improvement process.

How many metrics is too many?

More than 10 active metrics means nobody pays attention to any of them. Start with 5, max out at 8. If you want to add a new metric, consider retiring one. The goal is a focused dashboard that drives action, not a comprehensive data warehouse.

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