Delivery Hero is seeking a Data Engineer II to join our Berlin operations for Quick Commerce. The team focuses on improving vendors' experiences and streamlining the supply chain through data science and data engineering. You will design, build, and maintain features and data workflows that enable scalable and reliable rollouts, and you will collaborate with a diverse group of data scientists and engineers to deliver high-quality services for millions of daily orders. Responsibilities include developing features that deliver value to users, maintaining data pipelines, and guiding the evolution of the overall architecture by applying engineering best practices (TDD, CI/CD). You will work with data scientists and engineers to optimize model lifecycle, monitoring, and alerting, and develop CI/CD pipelines for ML models. Qualifications: 4+ years of experience in data or software engineering; strong Python; Docker, Kubernetes, Postgres; cloud platforms AWS or GCP; orchestration tools such as Airflow, Kubeflow, MLflow; experience building cloud-based, scalable architectures. Nice to have: monitoring/observability tools (Grafana, Datadog, Splunk), and familiarity with infrastructure as code (Terraform, Drone CI, GitHub Actions).

Delivery Hero is seeking a Data Engineer II to join our Berlin operations for Quick Commerce. The team focuses on improving vendors' experiences and streamlining the supply chain through data science and data engineering. You will design, build, and maintain features and data workflows that enable scalable and reliable rollouts, and you will collaborate with a diverse group of data scientists and engineers to deliver high-quality services for millions of daily orders. Responsibilities include developing features that deliver value to users, maintaining data pipelines, and guiding the evolution of the overall architecture by applying engineering best practices (TDD, CI/CD). You will work with data scientists and engineers to optimize model lifecycle, monitoring, and alerting, and develop CI/CD pipelines for ML models. Qualifications: 4+ years of experience in data or software engineering; strong Python; Docker, Kubernetes, Postgres; cloud platforms AWS or GCP; orchestration tools such as Airflow, Kubeflow, MLflow; experience building cloud-based, scalable architectures. Nice to have: monitoring/observability tools (Grafana, Datadog, Splunk), and familiarity with infrastructure as code (Terraform, Drone CI, GitHub Actions).