Senior Data Platform / DevOps Engineer (Agile, Digital Platforms)

We are looking for a skilled and motivated Data Platform / DevOps Engineer to operate and evolve our Global Data Platform. In this role, you will work with modern distributed systems and data technologies, ensuring reliable, secure, and scalable data pipelines. You will apply Agile and DevSecOps principles to continuously improve platform stability, automation, and delivery efficiency while collaborating closely with security, engineering, and cloud operations teams.

• Operate and maintain Global Data Platform components, including:

• VM servers, Kubernetes clusters, and Kafka
• Data and analytics applications such as Apache stack, Collibra, Dataiku, and similar tools

• Design and implement automation for:

• Infrastructure provisioning
• Security components
• CI/CD pipelines supporting ELT/ETL data workflows

• Build resiliency into data pipelines through:

• Platform health checks
• Monitoring and alerting mechanisms
• Proactive issue prevention and recovery strategies

• Apply Agile and DevSecOps practices to deliver integrated solutions in iterative increments
• Collaborate and liaise with:

• Enterprise Security
• Digital Engineering
• Cloud Operations teams

• Review system issues, incidents, and alerts to:

• Perform root cause analysis
• Drive long-term fixes and platform improvements

• Stay current with industry trends, emerging technologies, and best practices in data platforms and DevOps

• Minimum 5 years of experience designing and supporting large-scale distributed systems
• Hands-on experience with:

• Streaming and file-based ingestion (e.g., Kafka, Control-M, AWA)
• DevOps and CI/CD tooling (e.g., Jenkins, Octopus; Ansible, Chef, XL tools are a plus)

• Experience with on-premises Big Data architectures; cloud migration experience is an advantage

• Integration of Data Science workbenches (e.g., Dataiku or similar)

• Practical experience working in Agile environments (Scrum, SAFe)

• Supporting enterprise reporting and data science use cases

• Strong knowledge of modern data architectures:

• Data lakes, Delta Lakes, Data Meshes, and data platforms

• Experience with distributed and cloud-native technologies:

• S3, Parquet, Kafka, Kubernetes, Spark

• Programming and scripting skills:

• Python (required)
• Java / Scala / R
• Linux scripting, Jinja, Puppet

• Infrastructure and platform engineering:

• VM setup and administration
• Kubernetes scaling and operations
• Docker, Harbor
• CI/CD pipelines
• Firewall rules and security controls