Your question is Measure Engineering Success at AutoRABIT. 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).
AutoRABIT Holding’s engineering organization supports Vault, Backup & Recovery, and Release Management products used by enterprise Salesforce teams. Over the last 2 quarters, leadership saw mixed signals: feature throughput improved, but customer-reported incidents and escalations also rose.
In Q1, the team shipped 148 production changes, up from 112 in Q4 (+32%). Median lead time for change improved from 5.8 days to 3.9 days, and deployment frequency increased from 18 to 31 releases per month. However, Sev-1/Sev-2 incidents rose from 9 to 16, change failure rate increased from 11% to 18%, and mean time to restore service worsened from 74 to 126 minutes. Customer-facing outcomes also softened: enterprise account logo retention fell from 96.4% to 94.8%, NPS dropped from 41 to 35, and support tickets tagged "deployment failure" in AutoRABIT Release Management increased 27%.
The CTO asks you to define how engineering success should be measured, identify which metrics are leading vs lagging, and propose a scorecard that can be reviewed monthly by engineering managers without encouraging local optimization.
deployments: deployment_id, product, team_id, environment, created_at, deployed_at, rollback_flagincidents: incident_id, severity, product, start_time, resolved_time, root_cause_categoryjira_issues: issue_id, team_id, type, story_points, created_at, done_at, reopen_countsupport_tickets: ticket_id, account_id, product, issue_tag, created_at, csat_scoreaccounts_monthly: account_id, product, arr, renewal_status, nps, active_admins