How 8 weeks of platform engineering transformed a 120-engineer fintech's entire delivery capability — and gave their senior engineers their careers back.
When we first met this Series B fintech, they had 120 engineers and a deployment process that hadn't changed since they were a team of 12. Every production release was a 6-week coordinated affair: a spreadsheet of manual steps, two senior engineers managing the process end-to-end, and an all-hands freeze window every other Sunday night. Feature branches piled up. Engineers waited weeks to see their code in production. The feedback loop between writing code and validating it with real users was measured in months, not days.
The underlying infrastructure told the same story. A self-managed Jenkins cluster — owned, in practice, by one Principal Engineer who had become the single point of failure for the entire delivery system. No staging environment that accurately reflected production. No feature flags to safely test changes with partial traffic. When something went wrong in production (which happened regularly, given the manual nature of releases), mean time to recovery stretched to four hours as engineers tried to diagnose issues across a system with essentially no centralised observability.
Leadership had started to consider a hiring freeze to address "tech debt." Engineering managers were burning out. The two engineers responsible for managing releases had started looking at other jobs — not because of pay, but because they hadn't written meaningful code in over a year. The platform team, meant to enable velocity, had accidentally become the biggest constraint on it.
We spent the first two weeks mapping every touch point in the release process: which humans did what, which scripts existed, where the undocumented knowledge lived. We produced a bottleneck map ranked by impact and used it to sequence every subsequent sprint so that the highest-pain items were addressed first.
We migrated all pipelines from Jenkins to GitHub Actions, introducing per-service workflow files in version control, automated test gates, and container build + push automation. ArgoCD was deployed to manage the GitOps promotion flow from dev → staging → production with automatic drift detection and reconciliation.
Backstage was deployed as the IDP, seeded with the full service catalog and golden-path templates for new services. Unleash was stood up for feature flag management so engineers could ship code to production gated behind flags and gradually roll out to users. Vault replaced hardcoded credentials across the entire service mesh.
Prometheus, Grafana, and Loki were deployed as the unified observability stack. SLO-based alerting replaced the previous spray of threshold alerts. PagerDuty routing was configured so incidents reached the right on-call engineer immediately. Karpenter was deployed to replace managed node groups for cost-efficient autoscaling. Full runbooks and a 30-day post-launch support window closed the engagement.
| Metric | Before | After |
|---|---|---|
| Release Cycle | 6 weeks | 1 day |
| Deploy Frequency | 2× per month | 8× per day |
| Mean Time to Recovery (MTTR) | ~4 hours | ~4 minutes |
| Engineers on Release Management | 2 FTE (100% allocated) | 0 FTE (automated) |
| Time to onboard a new service | 3–4 weeks | 2 hours (Backstage template) |
"Our engineers now ship features instead of babysitting deployments. The platform team went from the most-hated to the most-loved team overnight. We were on track to lose two of our best people to burnout — instead, they're now leading the next phase of platform evolution internally. I can't overstate how much this changed the culture of the engineering org."— VP Engineering, Series B FinTech (name withheld per NDA)
30 minutes. We'll review your current deployment process and show you exactly where the bottlenecks are — and what it would take to fix them.