Mise en ligne le
06-01-2025
DataOps Engineer Date06-01-2025 Direction
DATAOPS ENGINEER ContratCDI LocalisationCasablanca FonctionAutre fonction 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.
