Cloud Platform Engineer

Compunnel, Inc.; Warren (Warren County); On-site; USD 100,000 - 130,000; Full time

Job summary

Compunnel, Inc. is seeking an experienced Cloud Platform Engineer to support cloud-native platforms and data engineering workflows within Azure and Databricks environments. The ideal candidate should have strong expertise in CI/CD automation, infrastructure provisioning, container orchestration, and cloud platform engineering.

Responsibilities include deploying and managing data engineering workflows, designing CI/CD pipelines, and ensuring platform reliability. Candidates should have hands-on experience with Databricks, Terraform, and scripting in Python and Bash.

Qualifications

• Strong expertise in writing clean, modular YAML configurations for CI/CD pipelines and Kubernetes.
• Proven experience in deploying and managing Databricks assets.
• Strong automation skills using Python, Bash, and YAML.

Responsibilities

• Manage and execute environment promotion and release management activities.
• Deploy and manage data engineering workflows within Databricks.
• Design and maintain CI/CD pipelines for code automation.

Skills

CI/CD automation

Terraform

Azure services

Kubernetes

Python

Bash

Databricks

Monitoring and observability solutions

Tools

Azure DevOps

GitHub Actions

Docker

Job description

We are seeking an experienced Cloud Platform Engineer to support the deployment, automation, and management of cloud-native platforms, data engineering workflows, and containerized applications within Azure and Databricks environments. The ideal candidate will have strong expertise in CI/CD automation, infrastructure provisioning, container orchestration, and cloud platform engineering.

This role requires hands-on experience with Databricks deployments, Terraform, Azure services, Kubernetes-based platforms, and modern DevOps automation practices to support scalable and reliable enterprise cloud solutions.

Key Responsibilities

• Manage and execute environment promotion and release management activities across development, testing, staging, and production environments.
• Deploy and manage data engineering workflows, machine learning models, applications, and agents within Databricks environments.
• Design, build, and maintain CI/CD pipelines to automate code validation, testing, deployment, and release processes.
• Implement monitoring, alerting, and observability solutions for Azure infrastructure and Databricks workflows to ensure platform reliability and availability.
• Develop reusable and modular YAML configurations for CI/CD pipelines, Kubernetes deployments, and automation workflows.
• Provision and manage cloud infrastructure and Databricks resources using Terraform modules.
• Support Azure container registries, orchestration platforms, networking configurations, and cloud-native infrastructure operations.
• Develop automation scripts and utilities using Python, Bash, and YAML.
• Troubleshoot deployment, infrastructure, networking, and automation issues across cloud platforms.
• Collaborate with engineering, DevOps, and data platform teams to improve platform scalability, automation, and operational efficiency.

Required Qualifications

• Strong expertise in writing clean, modular, and reusable YAML configurations for CI/CD pipelines and Kubernetes environments.
• Proven hands‑on experience deploying and managing Databricks assets using Databricks Asset Bundles (DABs) and Databricks CLI.
• Experience writing Terraform modules for cloud infrastructure provisioning and Databricks provider management.
• Strong experience with CI/CD orchestration tools such as Azure DevOps, Azure Pipelines, GitHub Actions, or GitLab CI.
• Strong scripting and automation skills using Python, Bash, and YAML.
• Hands‑on experience with Docker and containerized application deployments.
• Experience implementing monitoring and observability solutions for cloud and data platforms.
• Strong troubleshooting, analytical, and problem‑solving skills.

Preferred Qualifications

• Experience supporting enterprise‑scale cloud‑native platforms and data engineering ecosystems.
• Familiarity with machine learning deployment workflows and MLOps practices.
• Experience with Infrastructure as Code (IaC) and DevOps best practices.
• Strong collaboration and communication skills within cross‑functional teams.