Kubernetes Data Engineer Jobs

US data engineer and data platform openings whose parsed job description names Kubernetes as deployment infrastructure. About 425 active listings, concentrated in platform-team and SRE-adjacent DE roles. Helm, Argo Workflows, ArgoCD, and the KubernetesPodOperator pattern for Airflow are the dominant supporting context. Pure analytics-engineer roles usually do not appear here; platform-engineer-titled DE roles do.

Kubernetes Data Engineer Jobs

US data engineer and platform listings that name Kubernetes in the posting, scored against your profile with salary and layoff signals.

Frequently asked questions

How many Kubernetes data engineer jobs are listed at once?

Around 425 active listings name Kubernetes in the parsed JD. The set concentrates in platform-engineer and SRE-adjacent DE roles at companies that self-host data infrastructure on K8s rather than buying managed services. Roles at managed-service-heavy shops (a Snowflake plus Airflow Cloud plus dbt Cloud stack) usually do not tag K8s.

Is Kubernetes experience required or just nice-to-have for these jobs?

Mostly preferred rather than required. About 65 percent of Kubernetes-tagged DE listings name K8s under 'preferred' or 'nice to have'; 35 percent require it as core infrastructure. Required-K8s roles concentrate at companies running Spark-on-Kubernetes, Trino-on-Kubernetes, or self-managed Airflow on Helm.

What Kubernetes-specific topics come up in these data engineer interviews?

Three patterns. Pod resource requests vs limits and the eviction implications when a Spark executor exceeds its limit. Persistent volume claims for stateful workloads (where Spark shuffle data goes when ephemeral storage runs out). The KubernetesPodOperator for Airflow: when to use it instead of running tasks in the scheduler's Python environment, and how to manage secrets across the boundary.

What's the typical Kubernetes stack for these data engineer roles?

Three common shapes. Self-managed data plane: EKS or GKE or AKS plus Spark-on-K8s plus Airflow on Helm plus Argo Workflows. Specialized: K8s plus Trino plus Iceberg for self-managed lakehouse query layer. Platform layer: K8s plus ArgoCD plus Terraform for the broader data platform infrastructure that DEs interact with rather than own.

How does Kubernetes DE pay compare to non-K8s DE roles?

Roughly 10 to 20 percent above the equivalent non-K8s DE role at the same seniority, reflecting the platform-engineering skill premium. The premium is durable because the talent pool that combines deep data-engineering chops with deep K8s ops chops is small. Senior platform-DE-shaped roles typically cluster in the $200K to $280K base range.

Should I learn Kubernetes if I'm currently a SQL-and-dbt-shaped data engineer?

Only if you want to move toward platform engineering. Most modern-data-stack DE roles (Snowflake plus dbt plus Airflow Cloud) never touch K8s; the managed services handle it. The K8s investment pays off when you target self-hosted data platform roles at companies that decided managed services do not fit their cost, performance, or compliance constraints.

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