Senior Platform & Kubernetes Infrastructure Engineer

Digital Turbine, Inc.; Durham (NC); Hybrid; USD 130,000 - 190,000; Full time

Job summary

Digital Turbine, Inc. is seeking a Senior Infrastructure Engineer to design and operate large-scale distributed systems and data platforms, driving platform standardization and GitOps adoption.

The role focuses on Kubernetes-based infrastructure, data/ML tooling, and cost-efficient operations across cloud environments. Ideal candidates bring 8+ years in infrastructure, strong cloud experience (AWS or GCP), and hands-on expertise with Kubernetes, Spark, Databricks, and CI/CD.

Qualifications

• 8+ years in infrastructure, platform, or back-end engineering building robust distributed systems.
• Experience with major cloud provider (AWS or GCP) and Go/Java/Python/Scala.
• Hands-on experience building and operating Kubernetes infrastructure stacks.
• Familiarity with data/ML infra: Spark, Kafka, data lakes, Databricks, or similar tech.
• Experience with infrastructure-as-code, CI/CD, GitOps, and modern observability tooling.
• Operational maturity: owned production systems, on-call, and reliability improvements.

Responsibilities

• Design, build, and operate large-scale distributed systems and data platforms for reliability and scalability.
• Drive platform standardization (GitOps/ArgoCD, unified CI/CD pipelines).
• Build and operate Kubernetes-based infrastructure including Spark-on-K8s and data/ML tooling.
• Implement MLOps and data-pipeline capabilities with ML and Data teams.
• Lead on-call rotations, incident responses, and blameless postmortems; automate to prevent recurrence.

Skills

Distributed systems

Go

Java

Python

Kubernetes

CI/CD

Observability

Go/Java/Python/Scala

Tools

Kubernetes

Databricks

Spark

Kafka

Job description

Digital Turbine, Inc. is seeking a Senior Infrastructure Engineer to design and operate large-scale distributed systems and data platforms, driving platform standardization and GitOps adoption.

The role focuses on Kubernetes-based infrastructure, data/ML tooling, and cost-efficient operations across cloud environments. Ideal candidates bring 8+ years in infrastructure, strong cloud experience (AWS or GCP), and hands-on expertise with Kubernetes, Spark, Databricks, and CI/CD.