Trading Systems Engineer
crypto:applicationengineeringIC4IT
Compensation
Not disclosed
Flow Traders is looking for an experienced Engineer who can elevate our GCP‑focused platform-especially GKE-while maintaining strong Linux and networking fundamentals. Our trading platforms span both ultra‑low‑latency bare‑metal systems and modern cloud‑native services. This role bridges on‑prem and cloud environments, strengthens global platform standards, and drives high‑impact improvements that enhance reliability, reduce toil, and support time‑sensitive trading workloads.
What you will do
Serve as the US‑based SME for our GCP platform with a strong focus on GKE workloads—working within globally aligned standards while contributing your own expertise to improve Terraform‑driven infrastructure and shape cloud architecture across regions
Identify and drive engineering improvements by researching pain points, reducing toil, and delivering high‑impact solutions across our cloud and trading infrastructure.
Leverage automation and IaC to manage and optimize Linux and Kubernetes environments spanning bare‑metal, virtualized, and cloud‑native systems—streamlining workflows and reducing operational friction.
Strengthen platform reliability by improving global monitoring and alerting frameworks, incorporating adaptive and self‑healing mechanisms to elevate operational responsiveness.
Act as a senior escalation point for complex Linux/Kubernetes/Cloud issues, participating in on‑call and ensuring thorough, well‑reasoned incident resolution.
Provide regional technical leadership while collaborating with global stakeholders to define and implement platform standards, architectures, and large‑scale initiatives.
Use AI‑assisted engineering tools to accelerate analysis, troubleshooting, documentation, and operational automation.
What you need to succeed
7–10+ years operating complex production environments spanning Linux, Kubernetes, and cloud‑native infrastructure.
Deep Cloud experience, preferably within GKE or an equivalent stack (cluster ops, workload orchestratio