DataOps/MLOps Engineer

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Description

Building and operating a modern on-premise DataOps and Machine Learning platform for Data Engineering and Data Science teams. The platform is based on Kubernetes and supports scalable, high-availability workloads across data processing, analytics, and ML systems. The role combines DevOps engineering, modern data lakehouse engineering, and MLOps support Hands-on experience designing and managing Kubernetes clusters from scratch (on-premises preferred) Hands-on experience with Helm and ArgoCD for GitOps-based application delivery in Kubernetes environments Experience building CI/CD pipelines with Azure DevOps Experience with Apache Airflow, Apache Spark, and Apache Flink for pipeline orchestration and processing Experience with Trino or ClickHouse as distributed query engines Experience with dbt for data transformation and modeling Familiarity with modern open table formats (Iceberg) Familiarity with Dagster, JupyterHub, Feast, or Parquet-based data lake patterns will be a plus

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