Required Senior DevOps Engineer
About The Position
We are scaling rapidly, and we are transforming our internal infrastructure into a high-velocity, self-service product. This is a senior opportunity to architect the future of our platform, setting the technical standard for how we deliver services while ensuring reliability and developer independence across diverse global markets.
Responsabilities:
Self-service enablement: Build secure, automated pathways that accelerate development while keeping organizational standards intact.
Architectural modularization: Design Kubernetes-native abstractions - controllers and operators - and decouple infrastructure into independent service lifecycles that support rapid iteration.
Everything as code: Treat infrastructure, policy, and configuration with the same SDLC as application software - versioned, reviewed, tested, and CI-gated.
Proactive reliability: Own production through robust observability, clear SLOs, and high operational hygiene; define alerting that fires correctly and lead blameless postmortems that fix the system.
Requirements: Strong GCP experience at production scale (GKE, IAM, networking, Cloud SQL/BigQuery). No compromise here.
Kubernetes-native mindset: declarative-first, comfortable with controllers and operators, and a platform-engineering instinct for self-service and separation of concerns.
Everything-as-code discipline: any code ships through a defined SDLC (versioned, reviewed, tested, CI-gated). Terraform (KCC a plus), Helm, GitOps (ArgoCD or similar) in your toolkit.
Software engineering background, or proven strong development ability in Go and/or Python - you write production code, not just glue scripts.
Security awareness baked into how you build: least-privilege IAM, secret hygiene, policy and supply-chain gates, not bolted on after.
Production-first mindset: you design for reliability, observability, and recoverability, and you own what you ship.
Nice to have:
Policy-as-code (OPA/Conftest, Kyverno) and supply-chain/CI security tooling.
SLO/error-budget practice and DORA-based delivery measurement.
Multicloud experience (AWS / Azure) - GCP is home, but breadth helps.
Big Data or MLOps exposure (Airflow, Dagster, Ray, Kubeflow).
A point of view on AI-assisted and spec-driven development.
This position is open to all candidates.