
Platform Engineer Architect
- Madrid
- Permanente
- Tiempo completo
- Engage directly with clients
- Drive pre-sales technically: qualify opportunities, build proposal-level architectures, demo value, articulate trade-offs, and support deal closure.
- Lead end-to-end solution design; define and propagate reusable delivery patterns (“golden paths”) that reduce complexity and accelerate client velocity.
- Build, validate, and evolve prototypes/POCs in real client environments to de-risk decisions and push designs toward production.
- Embedded with client teams as needed—hands-on integration of platforms, CI/CD, infrastructure-as-code, automation, and operational tooling.
- Align and influence across multi-stakeholder ecosystems (client engineering, security, operations, procurement, internal delivery).
- Spot and articulate expansion or follow-on opportunities based on evolving client use and value realization.
- Contribute to our AI & Platform Engineering R&D roadmap: evaluate, prototype, and recommend selective AI-assisted SDLC enhancements (code suggestions, intelligent testing, automated reviews).
- Comfortable in pre-sales and commercial technical motions: qualification, value framing, objection handling, and closing support.
- Developer-level coding ability in at least one high-level language (Python, JavaScript/Node, Java, Go)—this is hands-on role, not advisory-only.
- Hands-on with some cloud-native stacks: Kubernetes (EKS/AKS/GKE), IaC, and CI/CD systems (ArgoCD, GitHub Actions, GitLab CI, Jenkins etc.)
- Hands-on with a Hyperscaler native Services (AWS, GCP or Azure)
- Rapid prototyping, debugging, and iteration in messy, real-world client contexts.
- Clear, direct communication tailored to both technical and executive audiences; able to push back constructively.
- Business fluency: ties architecture decisions to cost, risk, time-to-market, and client outcomes.
- Proven ability to build alignment across client and internal teams under pressure.
- External, client-facing solutions architecture or sales engineering experience
- Exposure to or ability to recommend AI-assisted SDLC tools/workflows to boost delivery speed and quality (used pragmatically).
- Background in consulting or services with measurable client impact.
- Relevant certifications (cloud provider, architecture, security etc).
- Experience in complex enterprise sales or procurement cycles.