https://bayt.page.link/kD4zUbgQz8huAMBm9
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الوصف الوظيفي

Contexte du recrutement et définition de poste

We are looking for an experienced DataOps Engineer to join our team. This role focuses on building and maintaining robust on-premise cloud infrastructures and data pipelines. You will work with a diverse range of tools and technologies, ensuring the reliability, scalability, and efficiency of our data and cloud ecosystems.


Key Responsibilities:


  • Data Stack Infrastructure Management: Deploy and maintain on-premise cloud environments using Hadoop, Kubernetes, Helm, and Minio.
  • CI/CD Implementation: Build and maintain infrastructure as code (IaC) with Terraform, automate configuration management with Ansible, and streamline CI/CD workflows using Jenkins and GitLab CI.
  • Monitoring and Observability: Oversee system performance using monitoring tools such as Grafana, Kibana, and ELK Stack to proactively address issues and ensure system health.
  • Data Operations Management: Oversee the performance of data pipelines and implement proactive measures to address issues.
  • Collaboration: Work closely with data engineers, data scientists, and stakeholders to align data delivery processes with business goals.
  • Machine Learning Workflow Optimization: Integrate tools like MLflow to manage and streamline machine learning workflows.
Profil recherché
  • Bachelor’s degree in Computer Science, Data Engineering, or a related field.
  • Proficiency in managing on-premise cloud environments with Kubernetes, Helm, and Minio.
  • Strong experience with Hadoop ecosystems for big data processing.
  • Experience with CI/CD tools, specifically Jenkins and GitLab Actions.
  • Familiarity with task scheduling and workflow orchestration tools such as Apache Airflow and Celery.
  • Hands-on expertise in Terraform and Ansible for infrastructure automation.
  • Strong problem-solving and communication skills.

Bonus:


  • Experience with monitoring and observability tools like Grafana, Kibana, and ELK Stack.
  • Experience with MLflow for machine learning lifecycle management is a plus.
  • Knowledge of Kafka for real-time data streaming and messaging.

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