5 days ago
Vila Nova de Gaia, Portugal +3 moreMid Level
Responsibilities
- Design, implement, and manage scalable cloud architectures in AWS.
- Build and maintain data pipelines, data systems, data warehouses, and data applications using ETL/ELT processes and schema modeling.
- Develop and maintain CI/CD pipelines for automated build, testing, and deployment.
- Implement infrastructure as code using Terraform, CloudFormation, or Pulumi.
- Manage Kubernetes and containerized workloads across EKS, AKS, and GKE.
- Automate operational tasks using Bash, Python, or Go.
- Monitor cloud environments with Prometheus, Grafana, ELK, CloudWatch, or Datadog.
- Apply cloud security practices involving IAM policies, network security, compliance, identity management, and access control.
- Collaborate with development teams to optimize cloud applications and improve DevOps workflows.
- Drive cloud cost optimization and FinOps practices.
- Support data governance, metadata management, data quality, and compliance.
Requirements
- At least four years of experience in data engineering roles building and maintaining data pipelines, systems, and applications.
- Degree in computer science, software engineering, or an engineering discipline.
- Proficiency in Python and one additional data engineering programming or querying language such as Java, SQL, or NoSQL.
- Experience with AWS and other cloud platforms.
- Strong CI/CD experience with Jenkins, GitHub Actions, GitLab CI/CD, or ArgoCD.
- Hands-on Kubernetes and container-orchestration experience.
- Strong SQL and NoSQL data modeling knowledge and distributed-systems design expertise.
- Experience with Spark or other big-data distributed-processing technologies and familiarity with Airflow.
- Advanced infrastructure-as-code knowledge using Terraform, CloudFormation, or Pulumi.
- Experience with observability and logging tools such as Prometheus, Grafana, CloudWatch, ELK, or Datadog.
- Experience with configuration-management tools such as Ansible, Chef, or Puppet.
- Knowledge of data governance, metadata management, data quality, cloud security, identity management, and access control.
- Familiarity with standard AI/ML processes and operations is valued.
- Fluency in English is mandatory; additional languages are advantageous.
- Strong communication, collaboration, analytical, problem-solving, and attention-to-detail skills.
Benefits
- Hybrid work arrangement.
- Multicultural and inclusive team environment with a supportive work-life-balance culture.
- Career growth programs and opportunities on national and international projects.
- Training and certifications in current technologies.
- Health and life insurance.
- Referral program with bonuses for talent recommendations.
- Access to offices in attractive locations.
Tech Stack
AnsibleApache AirflowApache SparkAWSBashChefDatadogGitHub ActionsGitLab CI/CDGoGrafanaJavaJenkinsKubernetesPrometheusPuppetPythonSQLTerraform
Categories
Data EngineeringDevOps
About Capgemini
Capgemini is a global IT services and consulting firm that delivers strategy, cloud, AI, software engineering, and managed services to large enterprises and public-sector clients. Founded in 1967 and headquartered in Paris, it is publicly traded on Euronext Paris and operates in 50+ countries. The group expanded its engineering capabilities by acquiring Altran in 2020, now operating as Capgemini Engineering.