Platform Engineer II

Join a leader in the radiation safety space as a Platform Engineer II on a brand new Enterprise Data and AI Platform team. This organization is one of the world's key providers of radiation safety equipment in both the nuclear energy sector and nuclear medicine, and is now building the foundation for the next phase in its journey: radiation safety augmented with modern capabilities of Data, Analytics, and AI. You'll be part of a team building a centralized data platform from scratch, combining the philosophies of reusable, self-service tooling from Platform Engineering with the technical expertise of data pipelines development, data modeling, and enterprise-grade data insights from Data Engineering. This platform will accelerate innovation in the industry, augmenting world-class hardware products with cutting-edge insights and AI capabilities.

This is a super unique opportunity to join a brand new team with a startup-like mentality while working in an established industry that's ripe for innovation. You'll build infrastructure from the ground up rather than maintaining legacy systems, giving you the chance to make architectural decisions and establish best practices from day one. The role offers significant autonomy and impact, working as a self-starter and jack-of-all-trades engineer who doesn't mind moving quickly while managing complex technical challenges and compliance restrictions inherent to the radiation safety and nuclear energy industry. You'll gain valuable experience building cloud infrastructure for data platforms using modern technologies like Terraform, while potentially working with Databricks and data engineering workloads. The combination of startup energy within an established organization provides both innovation opportunities and job stability. Working in the nuclear energy and nuclear medicine space offers exposure to highly regulated environments and mission-critical systems where your work directly impacts radiation safety worldwide. The focus on Data, Analytics, and AI means you'll be at the forefront of digital transformation in an industry traditionally dominated by hardware, positioning you for significant career growth as the platform scales.
Required Skills & Experience

• 2 to 4 years of experience in platform engineering, infrastructure, or DevOps
• Experience managing infrastructure in AWS or Azure cloud environments
• Experience writing Terraform for infrastructure provisioning
• Scripting ability in Python, Bash, or similar languages
• Understanding of cloud networking, security, and IAM concepts
• Ability to work as self-starter with startup mentality
• Comfortable building from scratch and moving quickly
• Strong problem-solving and troubleshooting abilities
• Ability to manage complex technical challenges
• Comfortable working in compliance-restricted environments

Desired Skills & Experience

• Experience with Databricks or other data platform infrastructure
• Experience with data engineering or past experience working as a data engineer
• Experience on data platforms or supporting data workloads
• Familiarity with observability tools such as Datadog, Prometheus, or similar
• Experience with GitOps workflows and infrastructure automation
• Background in supporting data engineering workloads
• Understanding of data pipeline architectures
• Experience in regulated industries (nuclear, healthcare, government)
• CI/CD pipeline development and automation
• Container orchestration with Kubernetes or Docker
• Infrastructure monitoring and incident response experience

What You Will Be Doing
Tech Breakdown

• 40% Infrastructure as Code (Terraform development, cloud resource provisioning)
• 25% CI/CD and Automation (pipeline development, deployment automation, scripting)
• 20% Data Platform Infrastructure (supporting Databricks, data engineering workloads)
• 15% Monitoring and Operations (observability, troubleshooting, incident response)

Daily Responsibilities

• 60% Hands-On Engineering (building infrastructure, writing code, deploying resources)
• 25% Automation and Tooling (developing pipelines, creating scripts, improving workflows)
• 15% Operations and Support (monitoring, troubleshooting, documentation, incident response)