Data Engineer (Kubernetes)

At Data Science Lab, we develop data platforms and AI solutions for organizations looking to advance. As a Data Engineer, you will build data platforms and pipelines while helping to integrate Kubernetes into our data architectures more effectively.

• Design and build cloud-native data platform architectures
• Deploy and manage data applications on Kubernetes
• Answer questions regarding the use of Kubernetes for both colleagues and clients
• Containerize data and AI workloads
• Automate data workflows and infrastructure with Infrastructure as Code
• Integrate tools such as Airflow, Spark or ML workloads into Kubernetes platforms
• Work with cloud platforms such as Azure, AWS or GCP
• Set up CI/CD pipelines
• Collaborate with data scientists and ML engineers to make models production-ready

• 3–6 years of experience as a Data Engineer, Platform Engineer or Software Engineer
• Strong experience with Kubernetes
• Experience with Docker and containerized workloads
• Experience with streaming data (e.g., Kafka)
• Programming experience in Python
• Experience with data pipelines
• Familiar with cloud platforms (Azure, AWS or GCP)
• Experience with CI/CD and DevOps practices
• Experience with tools such as Databricks, Airflow, dbt, Terraform or Spark is a plus

• Challenging projects at leading organizations
• Plenty of room for technical deepening and innovation
• Lab hours for experimentation and development
• A team of engineers, data scientists and strategists
• Direct influence on the growth of our engineering solutions