Senior Infrastructure Engineer

Digital Turbine • Durham, CA

1d • Hybrid

About The Position

The Tech Platform organization maintains and evolves the foundational platform and shared services that every other DT workstream depends on. As we assemble siloed products into a single marketplace, getting the platform right — reliable, observable, standardized, and cost-efficient — is the critical variable for execution at marketplace scale. As a Senior Infrastructure Engineer, you'll design and operate the large-scale distributed systems, data infrastructure, and deployment platforms that serve tens of billions of daily events. You'll help drive our OneDT standardization — including the migration to GitOps/ArgoCD and unified CI/CD — and build the platform capabilities (including ML and data infrastructure) that the rest of engineering builds on.

Requirements

• 8+ years in infrastructure, platform, or back-end engineering, with a track record of building robust distributed systems
• Deep experience with a major cloud provider (AWS or GCP; Azure a plus) and proficiency in Go, Java, Python, or Scala
• Strong hands-on experience building and operating Kubernetes infrastructure stacks.
• Familiarity with data and/or ML infrastructure: Spark, Kafka, data lakes, Databricks, or comparable technologies
• Experience with infrastructure-as-code, CI/CD, GitOps, and modern observability tooling
• Operational maturity: you've owned production systems, run on-call, and improved reliability systematically

Nice To Haves

• Experience leading large migrations or platform-standardization programs across multiple teams
• Background in high-throughput, low-latency systems such as real-time bidding or event streaming
• Experience with cost governance (FinOps) at scale

Responsibilities

• Design, build, and operate large-scale distributed systems and data platforms for reliability, scalability, and cost-efficiency
• Drive the OneDT platform standardization: GitOps/ArgoCD adoption, unified CI/CD pipelines, and a clear developer-ownership model across business lines
• Build and operate Kubernetes-based infrastructure, including Spark-on-K8s and data/ML platform tooling (e.g., Databricks ecosystems)
• Implement MLOps and data-pipeline capabilities — training, deployment, and serving infrastructure — in partnership with ML and Data teams
• Champion observability, automation, and cost-optimization initiatives that improve reliability while reducing spend
• Participate in on-call rotations, lead incident response and blameless postmortems, and prevent recurrence through automation and platform improvements

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed