Staff Platform Engineer

We’re looking for a Platform Engineer to join our Platform team, which builds shared infrastructure and delivery tooling across Fliff’s products. The team is responsible for Cloud Core, CI/CD, Platform Security, and support triage. In this role, you’ll take ownership of our CI/CD platform and help it scale, making architectural decisions, evaluating trade-offs, and adapting to changing requirements and production challenges. Your work will shape how software delivery and infrastructure operate across the engineering organization.
Responsibilities
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Own and evolve our CI/CD platform: GitHub Actions with self-hosted runners (RunsOn), plus an in-progress migration and refactor of our Jenkins setup ("Jenkins Next").
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Build and maintain Infrastructure as Code across AWS using Terraform / Terragrunt / Terramate, following reusable component patterns (CloudPosse-style).
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Design secure-by-default delivery pipelines: branch protection and PR approval rulesets, secrets management (SOPS), and prod-approval flows for critical paths.
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Drive our move from IAM users and long-lived access keys toward IAM roles, and help close foundational security gaps (open ports, unmanaged credentials, missing guardrails).
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Work with Kubernetes ( EKS ), Helm, and ArgoCD, and support our observability stack (Grafana, Prometheus) and developer platform (Backstage).
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Review and improve existing components — Terraform, pipelines, AWS infra — not only build new ones, and raise the bar on how the team delivers.
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Share the support/triage load and help reduce it through better automation and tooling.
Requirements
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Strong AWS experience and deep hands-on CI/CD expertise (GitHub Actions and/or Jenkins).
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Solid Infrastructure as Code background with Terraform (Terragrunt / Terramate a plus).
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Real Kubernetes / EKS experience, including Helm and GitOps (ArgoCD).
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A security-conscious mindset — comfortable reasoning about IAM , secrets, network exposure, and least-privilege access (a DevSecOps lean is a strong plus).
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The ability to design a solution, then adapt gracefully when constraints change ("now make it global", "now cost is the primary driver") — digging for context rather than getting flustered.
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Sound judgment on edge cases and incidents: traffic spikes 10x, a region goes down — how do you approach the change and handle the outage?
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Effective, deliberate use of AI tooling, with genuine understanding of what it produces and the ability to explain trade-offs it can't.