Cloud Control Plane Engineer

Teradata is a company that empowers organizations with better information through its Autonomous Knowledge Platform. The Cloud Control Plane Engineer will join an advanced research team to shape the next generation of Teradata’s platform, focusing on optimizing compute infrastructure across various environments and utilizing AI-assisted development tools.

Responsibilities:

• Own and evolve how compute infrastructure is provisioned and operates across heterogeneous environments, including bare metal, VMs, containers, and cloud platforms
• Define and tune system-level behavior across CPU, memory, storage, and network, including how workloads are scheduled and resources are utilized
• Design repeatable, auditable provisioning and deployment workflows using infrastructure-as-code tools (e. g. , Terraform, Pulumi, or similar)
• Shape runtime characteristics such as startup behavior, workload placement, and resource efficiency as systems scale
• Identify and resolve bottlenecks across I/O throughput, network behavior, and provisioning performance
• Use AI-assisted tools to explore and accelerate implementation, refining outputs into production-ready infrastructure

Requirements:

• Experience working at the systems or infrastructure layer, including OS behavior, runtime environments, or low-level system interactions
• Experience operating Kubernetes (or similar systems), including familiarity with scheduling behavior, node-level characteristics, and cluster lifecycle concerns
• Familiarity with containerization, virtualization, and infrastructure provisioning across cloud and on-premises environments
• Experience designing systems intended to run across multiple environments, with portability and consistency in mind
• Understanding of distributed system behavior, particularly how resource constraints affect performance and scalability
• Experience with observability, profiling, or performance analysis at the system level
• Comfort working in environments where system design and direction are actively evolving
• Exposure to query engines, columnar databases, or analytical data platforms (e. g. , Presto, Trino, Spark, DataFusion)
• Background in big data infrastructure or data pipelines - familiarity with the scale and volume of data these systems process
• Experience with eBPF, kernel performance tuning, or NUMA-aware hardware configuration
• Contributions to open-source infrastructure tooling