Middle DevOps Engineer
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.
About the client:
Our client is a technology company focused on software development and innovative engineering solutions. With a strong emphasis on quality, scalability, and modern architecture, the company builds reliable digital products that empower businesses across various industries.
About the role:
We are looking for a DevOps Engineerto join the team.
Requirements:
– Understanding of Python development.
– Understanding of creating and maintaining Python packages and dependencies.
– Working experience with PyTest and/or Contract Testing implementations.
– Expertise in unit, integration, and E2E test implementations for micro services and REST API’s.
– Expertise in creating maintainable, well-tested code with proper error handling.
– Solid understanding of RESTful composition and service endpoints.
– Experience with AWS, particularly with S3, EKS, ECR, Lambda, or similar services.
– Understanding in designing and implementing automated CI/CD workflows.
– Experience with GitHub Actions workflow design and implementation.
– Good understanding of Docker image composition/build and optimisation.
– Experience with Kubernetes.
– Familiarity with observability and monitoring, metrics collection/performance benchmarking.
Nice to have:
– Experience in GoLang development.
– Basic knowledge of JS/TS.
– On-hands experience with Rancher K3s.
– Experience with Terraform/terragrunt/pulumi/CDK or similar IaC instrumentation.
Responsibilities:
– Understand, maintain, and implement auth (oidc, saml) and other RBAC policies across organisation’s infrastructure.
– Build and own hybrid infrastructure across cloud and multiple on-prem environments.
– Maintain CI/CD pipelines for data migration, release management, and testing across multiple target environments.
– Maintain and optimise Docker/k8s environments for development and deployment.
– Develop Python libraries and APIs for internal orchestration.
– Implement data versioning and dataset management solutions.
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