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Assess Engineering Team Health Metrics

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Problem

Business Context

Figma's Core Product engineering organization has 14 teams and ~180 engineers. Over the last two quarters, leadership has seen mixed signals: roadmap delivery slipped from 82% to 68%, Sev-1 incidents increased from 3 to 8 per quarter, and voluntary engineering attrition rose from 6% to 11% annualized. The CTO wants a concise metric framework to determine whether an engineering team is healthy and operating effectively.

Metric Scenario

You are asked to define a scorecard for one product engineering team of 12 engineers. In the most recent quarter, the team shipped 21 planned stories out of 32 committed, median PR cycle time increased from 18 to 31 hours, deployment frequency fell from 5.2 to 3.1 deploys per week, escaped defect rate rose from 0.7 to 1.6 per 1,000 active users, on-call pages increased from 14 to 29, and eNPS for the team dropped from +24 to +5. However, feature adoption for the team’s flagship workflow increased from 41% to 47% of eligible users.

Requirements

  1. Define the primary metric or scorecard you would use to assess engineering team health.
  2. Identify which signals are leading vs. lagging indicators.
  3. Explain how you would decompose the metric to diagnose whether issues come from delivery, quality, reliability, or team sustainability.
  4. Propose threshold ranges for healthy, watchlist, and unhealthy performance.
  5. Recommend what actions leadership should take if the team is strong on product impact but weak on operational health.

Data Available

  • jira_issues: issue_id, team_id, story_points, committed_flag, completed_flag, created_at, resolved_at
  • github_prs: pr_id, team_id, author_id, created_at, first_review_at, merged_at, lines_changed
  • deployments: deployment_id, team_id, deployed_at, environment, rollback_flag
  • incidents: incident_id, team_id, severity, opened_at, resolved_at, root_cause
  • bugs: bug_id, team_id, detected_source, created_at, severity, affected_users
  • eng_survey: employee_id, team_id, survey_date, eNPS, burnout_score, role_tenure
  • hr_roster: employee_id, team_id, start_date, end_date, termination_type
  • product_usage: team_id, feature_name, eligible_users, active_users, date