Hybrid Cloud Data Engineer: AWS, Airflow, Kubernetes
Summary
Designs, builds, and maintains cloud-native data platforms on AWS to power business intelligence, analytics, and AI initiatives. Day to day involves developing Python data pipelines, managing Kubernetes workloads, orchestrating Apache Airflow workflows, and monitoring with Grafana.
Global Enterprise Partners is seeking an experienced Cloud Data Platform Engineer to join a large-scale enterprise transformation. The role focuses on designing, developing, and maintaining cloud-native data solutions that power business intelligence, analytics, and AI-driven initiatives.
You will design scalable data platforms in AWS, build robust data pipelines with Python, manage Kubernetes workloads, and implement Airflow-driven workflows with Grafana-based monitoring.
Design and implement scalable data platforms within AWS cloud environments. Develop and optimise data pipelines using Python and modern data engineering practices. Manage and support containerised workloads running on Kubernetes. Build, schedule, and monitor workflows using Apache Airflow. Implement monitoring, alerting, and observability solutions using Grafana and related tooling. Collaborate with data scientists, platform engineers, and business stakeholders to support AI and advanced analytics use cases. Ensure platform reliability, performance, scalability, and data quality. Contribute to automation, DevOps, and cloud engineering best practices. Proven experience delivering enterprise-scale data engineering solutions.