Infrastructure & Automation Engineer

Description

A technology company developing a next-generation data acceleration platform is looking for an Infrastructure & Automation Engineer to be the first engineer in this area, building and maintaining the automation and infrastructure behind its core technology. This is a highly hands-on role with significant ownership and the opportunity to shape the foundations of the environment from the ground up.

Responsibilities:

• Build robust automation frameworks for testing, validating, orchestrating, and releasing software.
• Develop internal tools in Python to automate complex workflows, debugging, testing, and release processes.
• Design and maintain CI/CD pipelines using GitHub Actions, ensuring every release meets high standards of correctness and performance.
• Build monitoring and observability across distributed systems using Prometheus, Grafana, and CloudWatch.
• Work hands-on with AWS and Kubernetes/EKS to build and operate scalable infrastructure.
• Investigate complex system failures and performance issues across the application, container, cloud, and Linux layers.
• Work closely with low-level systems and engineering teams, creating automated feedback loops that help drive system and hardware-level optimization.

Requirements

• 6 years of experience in Software Development, QA Automation, DevOps, or Infrastructure Engineering in cloud or distributed environments.
• Strong Python development skills, with experience building modular automation frameworks, tools, SDKs, or debugging systems.
• Hands-on AWS experience, particularly with EMR, S3, and EC2.
• Hands-on Kubernetes/EKS experience, including orchestration and networking.
• Experience building and maintaining CI/CD and automated release pipelines.
• Experience with observability and monitoring of distributed systems.
• Strong Linux and CLI skills, with the ability to troubleshoot issues across OS, containers, and infrastructure.
• Strong problem-solving skills and the ability to work independently in a technically challenging environment.

Advantages:

• Experience with Apache Spark, Iceberg, or large-scale distributed data systems.
• Background in performance testing, benchmarking, or systems optimization.
• Experience working with low-level, hardware, or systems engineering teams.