Mid-Level DevOps Engineer - Data Lake Project
What will you do?
The objective of the position is to maintain and optimize a fully automated, GitOps-driven cloud infrastructure. Act as a highly resolutive, self-driven operational backup in the absence of the DevOps & Cloud Manager, keeping production systems stable and secure with minimal supervision.
Maintain Code-Driven Infrastructure: Maintain and scale declarative Infrastructure as Code (IaC) solutions via Terraform across complex, large-scale codebases, prioritizing reusable module design, state optimization, and drift prevention.
Manage Automated Workflows & GitOps: Oversee the end-to-end lifecycle of Kubernetes clusters using GitOps practices (ArgoCD) and manage automated infrastructure workflows, ensuring zero manual intervention in the deployment pipeline.
Autonomous Incident Resolution: Drive technical incidents to resolution with a high degree of autonomy, performing deep root-cause analysis and utilizing automated monitoring/alerting frameworks to proactively maintain system health.
Enforce Rigorous Quality Standards: Ensure all infrastructure code, automated workflows, and configurations meet strict quality gates. Author and maintain comprehensive, crystal-clear technical documentation for all architectures and automation processes.
Support Data & AI Automation: Maintain the automated provisioning and scaling of infrastructure for Data and AI workloads, including GPU-enabled Kubernetes nodes and cloud-native AI pipeline components.
Enterprise SaaS Administration: Programmatically configure and manage enterprise platforms, including Bitbucket Cloud (RBAC, branch strategies) and SonarQube Cloud (Quality Gates).
Contribute to Future Migrations: Participate actively in the long-term planning and execution of our multi-year strategic migration from Azure to Google Cloud Platform (GCP) and from Argo Workflows to Azure DevOps pipelines.
What will you need?
Background: Degree holder in Computer Science, Software Engineering, or a closely related field (or 3-5 years of equivalent commercial experience in a SysAdmin/DevOps discipline).
Experience Tier: 3-5 years of commercial experience working as a DevOps Engineer in highly automated, multi-contributor enterprise environments.
Automation Workflow & GitOps Depth: Solid understanding of event-driven automation workflows and extensive hands-on experience running Kubernetes workloads using GitOps tools (ArgoCD or equivalent).
Large-Scale IaC: Proven experience handling Terraform inside large repositories, including clean state management and writing modular, linted, reusable code.
Cloud Platforms: 2+ years of administrative experience with Microsoft Azure cloud services. Familiarity with Google Cloud Platform (GCP) or Azure DevOps is a strong plus due to upcoming migration initiatives.
Containerization & Code Quality: Strong proficiency in Docker (creating secure, unprivileged, size-optimized containers) and familiarity with running code quality automation tools (like SonarQube).
Mindset: Highly resolutive problem-solver who takes strict ownership of tasks and thrives in an environment that requires minimal micromanagement.
Gain deep exposure to enterprise-scale multi-cloud migrations and modern AI infrastructure engineering. Learn new tools through hands-on proof-of-concept projects.