Engineering quality at scale, without slowing down
A global financial-data company was shipping from more than 30 engineering teams with no shared definition of quality. We stood up a technology governance model and a quality-engineering center of excellence that cut build-and-deploy times by 70 to 90%, reduced defects by about 60%, and moved regulatory compliance into the engineering process itself, across all 30-plus teams.
Regulated, high-scrutiny businesses face a tension they cannot wish away: they cannot trade speed for control, or control for speed. A global financial-data company lived at the sharp end of it. More than 30 engineering teams shipped into one shared platform, and each did it its own way. Quality varied from team to team, and defects slipped into production because nothing systematically caught them first.
The root cause was not talent. It was the absence of a shared standard. The company also operated under heavy regulation, with a large European footprint, so the Digital Operational Resilience Act (DORA) and the Markets in Financial Instruments Directive (MiFID II) were not optional, and their requirements could not be reconstructed in a report after release. Quality and compliance both had to live inside the engineering process, not sit beside it.
So we built that process. Across the portfolio, we established a Technology Governance Model and a Quality Engineering center of excellence that gave more than 30 teams one way to ship safely:
- Standardized the delivery toolchain: containerized workloads on Kubernetes and Docker, continuous integration and deployment through Jenkins and Spinnaker, static analysis with SonarQube, disciplined code review, and Kafka-based messaging, with defect-density and infrastructure profiling to show where quality actually broke down.
- Set portfolio-wide service level indicator (SLI) and service level objective (SLO) targets, and the controls behind them, so “good enough to ship” stopped being a matter of opinion.
- Wove the DORA and MiFID II requirements directly into the engineering and reporting workflows, so the evidence accrued as teams worked rather than being assembled afterward.
- Partnered with the chief operating officer (COO) and chief financial officer (CFO) to weigh the security, regulatory, cost, and long-term trade-offs of moving the core platform to a private cloud on Amazon Web Services (AWS), then proved the path through controlled pilots rather than one high-risk migration.
Once every team shipped the same way, the results showed up across the board.
Before and after
| Measure | Before | After |
|---|---|---|
| Build and deploy times | Manual and inconsistent | 70–90% reduction |
| Software defects | Frequent, slipping into production | ~60% fewer |
| Quality standard | Different on every team | One standard across 30+ teams |
| Regulatory evidence (DORA, MiFID II) | Assembled after release | Produced by the workflow |
Speed and control stopped being a trade-off once quality was engineered into the way teams worked rather than inspected after release. That is the through-line of our four-principle approach: process and technology carried the standard, education spread it across more than 30 teams, and quality ran through all of it. What the company gained was not a one-time cleanup but a single, repeatable way to ship that every team could rely on and a regulator could trust.