SRE Leadership Case Study: Establishing Reliability and Observability in a FinTech Platform
While working with a FinTech startup operating in a high-growth, fast-paced environment, my primary focus during my first week was not feature delivery, but platform stability and operational reliability. The company was running a microservices architecture with over a dozen production services, yet had no effective observability across infrastructure or applications.
At the time, outages were typically detected by customers reporting issues to customer support, not by engineering. This resulted in delayed incident response, extended downtime, and reputational damage. The situation was further compounded by the fact that several APIs were consumed by third-party partners. When these APIs failed, the impact propagated beyond our platform, disrupting collections and payment workflows across multiple external businesses and their customers.
It became clear that reliability was not just a technical concern but a direct business risk.
Phase 1: Infrastructure Observability and Alerting
The first decision was selecting an appropriate monitoring stack. Given that the platform was fully deployed on AWS, I chose Amazon CloudWatch over Prometheus/Grafana to leverage native integrations, centralized visibility, and streamlined alerting.
I deployed and configured the CloudWatch Agent across all production servers to collect critical metrics beyond default CPU usage, including:
CPU utilization
Memory utilization
Disk usage
This immediately expanded our observability footprint from single-metric visibility to full host-level telemetry.
I then designed a centralized dashboard providing real-time visibility across all production infrastructure. Based on historical behavior and service tolerances, I defined alert thresholds and configured alarms. For example:
Memory utilization ≥ 80% sustained for 5 minutes triggered immediate alerts to the DevOps/SRE team.
These alerts were enabled across all production servers.
Outcome:
Mean Time to Detect (MTTD) was reduced from customer-reported incidents to sub-5 minutes.
Infrastructure-related incidents were identified before customer impact in over 70% of cases within the first month.
Phase 2: Third-Party Connectivity Reliability
A major source of downtime was unstable VPN connectivity to third-party service providers. Prior to my involvement:
Only a single primary VPN tunnel existed.
There was no monitoring of tunnel health.
Failures often went unnoticed until services were already disrupted.
I configured CloudWatch to monitor VPN tunnel status and set alarms to trigger when a tunnel remained down for three consecutive minutes. This provided immediate visibility into connectivity degradation and allowed rapid response.
Outcome:
VPN-related downtime was reduced by over 60%.
Incident response became proactive rather than reactive, with engineers alerted before full service disruption occurred.
Phase 3: Application-Level Health Monitoring and Alerting
Infrastructure visibility alone was insufficient without insight into application health. To address this, I implemented Route 53 health checks for all publicly accessible API endpoints.
Health checks validated endpoint availability and correctness, and alarms were configured as follows:
Production: Alert on non-200 responses for 3 consecutive minutes
Non-production: Alert on non-200 responses for 15 consecutive minutes to reduce noise
This approach ensured high signal-to-noise alerts while maintaining strict availability standards in production.
Outcome:
Mean Time to Recovery (MTTR) improved by approximately 40%.
Prolonged outages were virtually eliminated.
Engineering teams gained clear, actionable insight into service health.
Leadership and SRE Impact
Beyond the technical implementation, this initiative established a reliability-first culture. Monitoring and alerting shifted from being an afterthought to a core engineering concern. Engineers no longer relied on customer complaints to detect failures, and incident response became faster, data-driven, and more coordinated.
Overall impact included:
Reduced customer-visible downtime
Improved third-party trust and SLA adherence
Faster incident detection and resolution
A scalable observability foundation to support future growth
This work laid the groundwork for sustained reliability and positioned the platform to scale without sacrificing stability, aligning engineering outcomes directly with business continuity and customer trust.
PS: Ignore the numbers in the attached image, they’re just from a server stress test while I was showing my students how monitoring and alerting works. Chaos was intentional 😅
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