Your question is Measure Engineering Team Success. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Nimbus is a B2B workflow SaaS company with 320 employees and 55 engineers across 6 product squads. The CTO says engineering output looks high, but customer-facing reliability and delivery confidence have become inconsistent over the last two quarters.
In Q2, the team shipped 148 production deployments, up from 110 in Q1 (+35%). However, Sev-1 incidents increased from 3 to 8, average lead time for changes rose from 2.4 days to 4.1 days, and sprint commitment reliability fell from 84% to 68%. Product leadership is asking for a clear framework to measure engineering team success without over-indexing on velocity alone. Customer outcomes also moved: enterprise logo churn increased from 1.8% to 2.6%, and NPS for product reliability dropped from 41 to 34.
You are asked to define the right KPI set for the engineering organization and explain how to interpret trade-offs between speed, quality, reliability, and business impact.
deployments: deployment_id, service_id, squad_id, deployed_at, rollback_flag, change_sizeincidents: incident_id, severity, service_id, started_at, resolved_at, root_causejira_issues: issue_id, squad_id, issue_type, story_points, created_at, completed_at, sprint_idsprints: sprint_id, squad_id, committed_points, completed_points, sprint_start, sprint_endcustomer_accounts: account_id, segment, churned_flag, renewal_date, nps_reliability_scoreengineering_survey: engineer_id, squad_id, quarter, engagement_score, burnout_risk