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Platform Engineering Team Lead
PresencialTiempo completoDevOps & SREEnterprise Software
Lead and grow a platform engineering team responsible for transforming an operated infrastructure environment into a scalable, Kubernetes-native, self-service platform. Own platform architecture, reliability, developer enablement, and engineering standards for a growing global organization.
Responsabilidades
- Build and grow the platform engineering team.
- Create secure, automated self-service pathways for cross-functional engineering teams.
- Modularize infrastructure into a scalable architecture with independently managed services.
- Establish an Everything-as-Code approach for infrastructure, policy, and configuration.
- Drive observability, service-level objectives, operational hygiene, and high availability.
- Define technical ownership for core infrastructure and scale the global team.
- Mentor engineers, support autonomy, and establish sustainable delivery practices.
- Maintain hands-on technical involvement in architecture and difficult engineering decisions.
Requisitos
- Strong professional experience in Platform Engineering or DevOps, including hands-on Kubernetes in production.
- Experience leading engineers through mentoring, hiring, and team development.
- Cloud-native and Kubernetes-native approach using declarative systems, GitOps, controllers, and abstractions.
- Proficiency in Python, Go, and Bash sufficient to build tooling and review engineering work.
- Production-scale multi-cloud experience, including GKE, Cloud SQL, and BigQuery.
- Experience with Terraform, KCC, Helm, and GitOps delivery tools such as Argo CD.
- Product mindset focused on internal engineers, self-service, and secure delivery.
- Understanding of AI-assisted and specification-driven development practices.
- Commitment to blameless postmortems, root-cause analysis, and continuous learning.
Se valora
- AWS or Azure experience in addition to GCP.
- Policy as code using OPA, Conftest, or Kyverno.
- Supply-chain security and CI security experience.
- SLO and error-budget practices.
- DORA-based delivery measurement.
- Big Data or MLOps exposure, including Airflow, Dagster, Ray, or Kubeflow.