Big Data System Engineer

Infrastructure Domain experience must have.

• You are operating Global Data Platform components (VM Servers, Kubernetes, Kafka) and applications (Apache stack, Dataiku and similar)
• Implement automation of infrastructure, security components, and Continuous Integration & Continuous Delivery for optimal execution of data pipelines (ELT/ETL).
• Apply Dev Ops & Agile approaches to deliver the holistic and integrated solution in iterative increments.
• Liaison and collaborate with enterprise security, digital engineering, and cloud operations to gain consensus on architecture solution frameworks.
• Review system issues, incidents, and alerts to identify root causes and continuously implement features to improve platform performance.
• Be current on the latest industry developments and technology trends to effectively lead and design new features/capabilities.

• You have 5+ years of experience in building or designing large-scale, fault-tolerant, distributed systems
• Integration of streaming and file-based data ingestion / consumption (Kafka, Control M, AWA)
• Experience in Dev Ops, data pipeline development, and automation using Jenkins and Octopus (optional: Ansible, Chef, XL Release, and XL Deploy)
• Experience predominately with on-prem Big Data architecture
• Hands‑on experience/knowledge in integrating Data Science Workbench platforms (e.g. Dataiku) is good‑to‑have.
• Experience of agile project management and methods (e.g., Scrum, SAFe)
• Supporting all analytical value streams from enterprise reporting (e.g. Tableau) to data science (incl. ML Ops)

• In‑depth knowledge and experience in one or more large scale distributed technologies including but not limited to: S3/Parquet, Kafka, Kubernetes, Spark
• Expert in Python and Java or another static language like Scala/R, Linux/Unix scripting, Jinja templates, puppet scripts, firewall config rules setup
• VM setup and scaling (pods), K8S scaling, managing Docker with Harbor, pushing Images through CI/CD