Platform Engineer( On-Premises)

Role overview

Work arrangement

• Work location is Saudi Arabia; work mode is on-site or hybrid based on project requirements.
• Environment includes on-premises Development, QA, and Production systems.
• Engagement duration is listed as 3–5 months.

Key responsibilities

• Install, configure, deploy, and maintain on-premises data platform components.
• Perform daily platform administration, monitoring, maintenance, upgrades, and operational support.
• Deploy and support components on VMware-based infrastructure; integrate configurations and deployment processes with Git.
• Integrate platforms with IAM and PAM and support operations across Development, QA, and Production.
• Monitor availability, performance, capacity, resource utilization, and platform health.
• Troubleshoot platform, infrastructure, deployment, configuration, storage, network, and integration issues.
• Support containerized components with Docker and/or Kubernetes where applicable.
• Collaborate with Data Engineering, Infrastructure, DevOps, Security, Network, and Application teams to maintain platform stability, security, and availability.

Must-have qualifications

• 3–5 years of relevant experience in Data Platform Engineering, infrastructure, or Platform Administration.
• Hands-on on-premises platform installation and configuration experience and strong Platform Administration experience.
• Experience with VMware-based infrastructure and Git integration for deployment/configuration processes.
• Experience with enterprise security integrations involving IAM and PAM.
• Knowledge of deployment, configuration, maintenance, upgrades, monitoring, and troubleshooting.
• Experience supporting Development, QA, and Production environments.
• Understanding of infrastructure, networking, storage, backup/recovery, and security concepts for enterprise data platforms.
• Exposure to Docker and/or Kubernetes for containerized deployments.
• Bachelor's degree in Computer Science or a related field.

Good-to-have skills

• Experience with MinIO and Apache Iceberg; Starburst or Dremio; Apache Spark; or Informatica.
• Understanding of modern Data Lake and Data Lakehouse architectures.

Employment and engagement