Senior Infrastructure Engineer

Digital Turbine•Durham, CA

12d•Hybrid

About The Position

The Tech Platform organization maintains and evolves the foundational platform and shared services used by DT workstreams. As DT brings siloed products together into a single marketplace, the platform must be reliable, observable, standardized, and cost-efficient to support execution at marketplace scale. The Senior Infrastructure Engineer designs and operates large-scale distributed systems, data infrastructure, and deployment platforms serving tens of billions of daily events. The role helps drive OneDT standardization, including migration to GitOps/ArgoCD and unified CI/CD, and builds platform capabilities—including ML and data infrastructure—that other engineering teams use.

Requirements

At least 8 years of experience in infrastructure, platform, or back-end engineering, with a track record of building robust distributed systems.
Deep experience with AWS or GCP; Azure experience is a plus.
Proficiency in Go, Java, Python, or Scala.
Strong hands-on experience building and operating Kubernetes infrastructure stacks.
Familiarity with data and/or ML infrastructure such as Spark, Kafka, data lakes, Databricks, or comparable technologies.
Experience with infrastructure-as-code, CI/CD, GitOps, and modern observability tooling.
Operational maturity, including ownership of production systems, on-call responsibilities, and systematic reliability improvements.

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.
Azure cloud experience.

Responsibilities

Design, build, and operate large-scale distributed systems and data platforms for reliability, scalability, and cost-efficiency.
Drive OneDT platform standardization, including 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-Kubernetes and data/ML platform tooling such as Databricks ecosystems.
Implement MLOps and data-pipeline capabilities, including training, deployment, and serving infrastructure, in partnership with ML and Data teams.
Champion observability, automation, and cost-optimization initiatives to improve reliability and reduce spending.
Participate in on-call rotations, lead incident response and blameless postmortems, and prevent recurrence through automation and platform improvements.