Healthcare provider data infrastructure is a distributed systems problem at scale. Hundreds of upstream integrations, inconsistent data sources, and evolving workloads all introduce operational complexity and reliability risk.
Reliability and observability at scale. You’re operating a platform hundreds of integrations depend on. How do you maintain uptime, reduce alert fatigue, and build actionable observability across GKE and Cloud Run without drowning in noise? Meaningful SLIs, error budgets, and data quality signals — not just p99 latency.
Scaling infrastructure efficiently. As platform usage grows, infrastructure costs and operational complexity grow with it. You’ll improve autoscaling behavior, resource utilization, and workload efficiency across cloud-native distributed systems.
Incident response and operational maturity. Production incidents are inevitable; operational chaos is optional. You’ll own incident response processes, root cause analysis, escalation workflows, and runbooks — and make hard problems not happen again.
Infrastructure automation and developer velocity. You’ll build and maintain Infrastructure as Code, CI/CD pipelines, and operational tooling that reduce manual work and improve engineering productivity without sacrificing reliability.
Reliability engineering for data platforms. Uptime isn’t enough — you need to know when a provider record is stale, a pipeline is lagging, or a workload is behaving unexpectedly. You’ll instrument data freshness and infrastructure health, not just service uptime.