Key skills
GCPKubernetesNetworking fundamentalsAWSTerraformArgoCDCuriositycommunicationCross team collaborationProblem solvingSQLAIBigQueryGKESQSPostgresSQL ServerRabbitMQLoad BalancingCommunicationCollaboration
About this role
Array is a financial innovation platform that helps digital brands and financial institutions deliver consumer products to market faster. As an Infrastructure Engineer, you will build, operate, and scale cloud environments, focusing on GCP while collaborating with various teams to improve platform health and reliability.
Responsibilities:
- Take cloud problems, investigate deeply, explain root causes clearly, and propose practical solutions
- Own work end to end, drive it to completion, and communicate progress consistently
- Build, operate, and improve GCP infrastructure, with some AWS support as needed
- Support Kubernetes workloads on GKE and help teams run reliable, cost effective systems at scale
- Work with core GCP building blocks including:
- Kubernetes and containerized workloads
- Queueing and messaging systems such as Google Pub Sub, Amazon SQS, RabbitMQ, or similar
- Relational database systems like Postgres and Microsoft SQL Server
- Data and storage systems such as object storage and BigQuery
- Networking components including load balancers, web application firewalls, and traffic routing
- Use logs, metrics, and tracing to troubleshoot production systems and improve performance and reliability
- Identify opportunities to simplify architecture, reduce operational overhead, and improve scalability
- Bring curiosity and follow through to understand how cloud resources work and why they behave the way they do
- Maintain a habit of using AI tools to think, build, and ship faster—it’s your default, not an afterthought
Requirements:
- 2+ years of hands on experience operating production systems in GCP
- Working knowledge of AWS or another major cloud provider
- Strong fundamentals in networking, compute, storage, identity, and permissions
- Kubernetes experience, especially operating workloads in production
- Familiarity with scalable system components such as queues, databases, storage, and load balancing
- Comfort troubleshooting live systems using telemetry and evidence, not guesswork
- Ability to take ambiguous requests and turn them into clear plans and deliverables
- Strong communication and cross team collaboration skills
- A belief that AI is reshaping work, you instinctively use it to accelerate everything you do