Our requirements
* Platform automation and CI/CD: Ability to build reliable platform pipelines for provisioning, validation, deployment, policy checks, release management, and operational lifecycle management.
* Data and AI platform services: Practical knowledge of GCP services relevant to data and AI platforms, especially Vertex AI, BigQuery, Dataflow, Pub/Sub, Cloud Storage, Cloud Composer, and related integration patterns.
* Observability and operations: Experience with Cloud Logging, Cloud Monitoring, alerting, SLOs, incident response, resilience patterns, cost controls, and production-readiness practices.
* Hands-on technical leadership: Ability to set technical direction while contributing hands-on, coaching engineers, defining standards, documenting reusable patterns, and communicating complex platform decisions clearly to stakeholders.
Who you are
This is the right role for you if you:
* Are a GCP platform expert: You have proven experience designing, building, and operating production-grade platform capabilities on Google Cloud Platform.
* Are engineering-driven: You treat infrastructure as software and are highly comfortable with Terraform, automation, CI/CD, and modern DevOps practices.
* Understand secure foundations: You have expert-level knowledge of infrastructure, provisioning, identity provider integration, IAM, access control, networking, and platform security patterns.
* Lead by doing: You combine deep technical expertise with hands-on delivery, practical guidance, and the ability to raise engineering quality across the team.
* Stay ahead of the curve: You have a genuine interest in AI platforms, generative AI, and agentic workflows, and you know how to adopt new capabilities pragmatically and safely.
What we're looking for:
Education and experience:
* A BSc, MSc, or PhD in Computer Science, Engineering, Data Science, Information Security, or a related discipline, or equivalent practical experience.
* 10+ years of experience in cloud, infrastructure, platform engineering, DevOps, or similar technical leadership roles.
* Documented experience providing hands-on technical leadership and setting engineering direction in complex technology environments.
Required qualifications:
* Expert-level knowledge of GCP infrastructure, provisioning, automation, and Infrastructure as Code, with Terraform as an essential skill.
* Strong experience with GCP services and ecosystems relevant to data and AI platforms, such as Vertex AI, BigQuery, Dataflow, Cloud Composer, Cloud Run, GKE, Pub/Sub, Cloud Storage, Cloud Logging, and Cloud Monitoring.
* Strong understanding of IdP integration, IAM, federation, workload identity, secrets management, and secure access patterns in enterprise environments.
* Experience building production-grade CI/CD pipelines, observability, and operational controls for platforms.
* Ability to evaluate trade-offs between speed, cost, scalability, resilience, and compliance.
* Strong communication skills, including the ability to explain platform decisions, document technical patterns, and act as a hands-on subject matter expert for GCP infrastructure and Cloud AI Platforms.
Optional
- GCP infrastructure foundations: Strong hands-on experience with GCP organization, folders and projects, landing-zone patterns, Shared VPC, Cloud NAT, firewall policies, private connectivity, and environment isolation.
- Identity, access and security: Expert understanding of IAM design, least-privilege access, federation, workload identity, service accounts, secrets management, key management, and policy guardrails in enterprise environments.
- Infrastructure as Code: Excellent Terraform skills, including modular design, reusable patterns, testing, versioning, state management, and promotion across environments.
Your responsibilities
We are looking for an experienced, hands-on Tech Lead to join our Data Mesh and AI Platforms team, with a specific focus on infrastructure and platform capabilities on Google Cloud Platform (GCP). You will shape, build, and operate secure, automated, and reusable GCP foundations that enable teams across Nordea to provision and run modern data and AI solutions consistently and at scale.
About our team
Welcome to the Data Mesh and AI Platforms team. We are building the next generation of cloud-native data and AI platform capabilities for Nordea, with a strong focus on secure infrastructure, automated provisioning, identity integration, reusable engineering patterns, and operational resilience.
Your main responsibilities will include, but are not limited to:
* Set the engineering direction for GCP infrastructure and AI platform capabilities while staying hands-on in the design, build, and validation of core platform patterns.
* Spend a significant part of your time in hands-on engineering, including Terraform, CI/CD, GCP configuration, troubleshooting, and production-readiness improvements.
* Lead by example with hands-on engineering work across GCP infrastructure, platform automation, CI/CD, provisioning, and reusable infrastructure-as-code patterns.
* Design, mature and operate secure GCP platform foundations, including organization and project structure, landing-zone patterns, identity provider integration, IAM, networking, observability, logging, and policy guardrails.
* Build reusable Terraform modules and platform patterns that enable teams to provision reliable GCP environments with clear standards for access, networking, secrets, monitoring, cost, and lifecycle management.
* Drive pragmatic adoption of GCP data and AI platform services, with a focus on Vertex AI, BigQuery, Dataflow, and related services, ensuring they can be used safely in a banking-grade environment.
* Coach engineers through hands-on collaboration, code reviews, design reviews, and practical delivery of reusable platform patterns.
* Collaborate with product owners, data scientists, architects, cybersecurity, and governance teams to align technical decisions with business priorities and regulatory expectations.
* Own and communicate the technical roadmap for GCP infrastructure and platform capabilities, balancing speed, scalability, cost, compliance, and operational reliability.
* Translate technical complexity into clear recommendations, decision points, and status updates for both technical and non-technical stakeholders.