About the role
About CertifyOS CertifyOS is building the data infrastructure that powers modern healthcare. Today, healthcare organizations rely on fragmented and outdated provider data. This creates unnecessary administrative work, regulatory risk, and higher costs across the system. We’re solving that problem. Our API-first platform automates provider licensing, enrollment, credentialing, and network monitoring by connecting directly to hundreds of primary data sources. We help healthcare organizations maintain accurate, compliant, and reliable provider networks at scale. Our vision is simple: One API. One provider ID. Frictionless provider data. We’re backed by leading investors and built by a team with deep experience in provider data systems. At CertifyOS, we value authenticity, accountability, collaboration, results, and openness to feedback. We’re building a high-ownership team focused on solving real infrastructure problems that impact millions of patients.
About
the Role We’re looking for a Senior Site Reliability Engineer who takes ownership seriously — someone who designs for reliability, ships the automation, and stands behind it in production. You’ll work across cloud-native infrastructure on systems that process millions of provider records. This is a role with real scope: you’ll own the operational lifecycle end-to-end and influence platform architecture, reliability standards, and deployment workflows across systems that matter.
How We Work We ship fast, but we don’t ship sloppy. SREs at CertifyOS own the full lifecycle of what they support — from infrastructure design and deployment automation through observability, incident response, and postmortems. We use AI-assisted tooling aggressively to reduce toil and accelerate troubleshooting, which raises the floor on the problems we tackle — not an excuse to reduce rigor. If you do your best work reacting to incidents, this probably isn’t the right fit. If you do your best work preventing them, we should talk. Problems You’ll Solve 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. What We’re Looking For Reliability engineering fundamentals 5+ years in SRE, DevOps, Platform Engineering, or Infrastructure Engineering — operating production systems at scale where your infrastructure is someone else’s dependency and failures have real downstream consequences Track record of improving reliability end-to-end: you’ve debugged hard production problems, made them not happen again, and built the alerting to prove it Strong Linux systems administration, incident response, and root cause analysis skills Comfort influencing operational standards and mentoring teams on reliability practices Cloud infrastructure & platform engineering Deep hands-on experience with GCP — GKE, Cloud Run, and containerized workloads at scale Experience building and maintaining Infrastructure as Code with Terraform and/or Pulumi Fluency across deployment patterns and the judgment to know when each fits: rolling deployments, blue/green, canary — and the rollback story for each Experience with autoscaling, resource optimization, and infrastructure efficiency for distributed systems Experience managing infrastructure security, secrets, and access controls in regulated or security-conscious environments Observability & operational excellence Strong understanding of Golden Signals monitoring — latency, traffic, errors, saturation — and how to make them actionable rather than noisy Experience designing SLIs, SLOs, error budgets, alerting strategies, dashboards, and escalation workflows Hands-on experience with observability platforms: Google Cloud Monitoring, Datadog, Grafana, Prometheus, or similar Strong sense of data platform health: lineage, freshness, and correctness matter as much to you as throughput Automation & software delivery Experience building and maintaining CI/CD pipelines using GitHub Actions or similar Scripting or programming fluency in Python, Bash, Go, or similar — you reduce toil through code, not process Experience working with Git workflows and modern software delivery practices Communication & compliance Strong written and verbal communication — you can explain an operational risk to an engineer and a product manager in the same conversation Experience operating systems handling sensitive data or PII in regulated or compliance-adjacent environments