Principal Platform Engineer

Join ISS STOXX as a Principal Platform Engineer, where you will shape the evolution of our multi-cloud platform strategy, support our AI transformation, and collaborate with various teams to solve complex technical challenges. You will have a significant impact on the architecture, operational excellence, and long-term success of our global cloud platforms.

Missions

• Concevoir, construire et faire évoluer les capacités de la zone d'atterrissage AWS, en veillant à ce qu'elles soient conformes aux normes, à l'automatisation, à la gouvernance et à l'expérience développeur établies avec notre plateforme GCP.
• Développer et améliorer les services de plateforme, les capacités d'auto-service, les outils CI/CD, les solutions d'observabilité et les cadres d'automatisation qui améliorent la productivité des développeurs et l'efficacité opérationnelle.
• Agir en tant qu'autorité technique pour les défis complexes d'ingénierie de plateforme, en fournissant un leadership pratique lors d'incidents critiques et en guidant la résolution de problèmes techniques à fort impact.

Profil recherché

- Willingness to participate in out-of-hours support and major incident escalation when required

- At least 10 years' experience in platform engineering, cloud engineering, or related disciplines, with a proven track record of designing and operating large-scale enterprise platforms

- Strong software engineering and automation skills, with expertise in Python and experience building reusable tooling and platform services

- Strong hands-on expertise with Infrastructure as Code, with master proficiency in Terraform

- Experience with modern platform and engineering tooling such as GitHub Actions, Apigee, Airflow, and related cloud-native technologies

- Proven ability to influence technical direction, build consensus, and drive adoption of engineering best practices across multiple teams

- Experience developing and operating large-scale CI/CD, automation, and platform engineering capabilities that improve developer productivity and operational excellence

- Experience building or maturing cloud landing zones, platform governance frameworks, and self-service developer platforms

- Practical experience enabling AI and data-driven workloads on cloud platforms, including familiarity with the tooling, operational considerations, and governance requirements associated with modern AI solutions

- A pragmatic, outcome-focused approach with a passion for simplicity, automation, observability, and continuous improvement

- Expertise with observability platforms such as Data Dog, Prometheus, Grafana, ELK, Splunk or equivalent, including monitoring, logging, tracing, reliability engineering, and incident management

- Experience coaching and mentoring engineers, fostering technical excellence, continuous learning, and collaborative problem-solving

- Experience working effectively within global, distributed, or multinational engineering organisations

- Experience evaluating emerging technologies, developing proof-of-concepts, and translating successful concepts into production-ready platform capabilities

- Sound technical judgement with the ability to balance innovation, risk, operational excellence, and business outcomes

- Deep understanding of modern cloud architectures, including containers, Kubernetes, serverless technologies, APIs, networking, security, and identity management

- Excellent communication, documentation, and stakeholder engagement skills, with the ability to explain complex technical concepts to both technical and non-technical audiences

- Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience

- Experience operating in multi-cloud environments and helping organisations establish consistent platform standards across cloud providers

- Experience with GCP AI services, vector databases, retrieval-augmented generation (RAG), model operations, or AI governance frameworks

- Experience building platforms that support AI-powered applications, machine learning workloads, or agentic systems