DevOps Engineer (Big Data Project)

This project is in the area of Big Data for a leading global gaming company. The organization has a strong local presence and maintains trusted relationships with regulators and governments in over 100 countries. It delivers value by maintaining high standards of professionalism, ethics and accountability. 

Our expectations

• Solid experience with AWS, particularly in networking with strong emphasis on Transit Gateway, infrastructure management and solid understanding of AWS access control (IAM)
• Proficiency in Terraform for IaC and dynamic configuration.
• Hands-on experience with Puppet in an on-premise setting.
• Good understanding of Big Data technologies (Hadoop, Spark, Hive, etc.).
• Familiarity with Grafana for monitoring setup.
• Strong troubleshooting skills across hybrid environments (AWS + on-premise).
• Flexibility and collaborative mindset to work in a multi-stakeholder setup with ongoing evolution.

Nice to have

• Basic knowledge of Docker
• Familiar with Vault and Ansible
• Good knowledge of Languages: Bash +Python

Main responsibilities

Cloud (AWS) Responsibilities – Data Lake Focus:

• Manage and maintain all AWS infrastructure components related to the Data Lake environment.
• Design, implement, and optimize network configurations in AWS to ensure connectivity and reliability.
• Release and deploy new versions of infrastructure and services, ensuring smooth and secure rollouts.
• Implement ad-hoc infrastructure changes based on developer needs, primarily using Terraform.
• Participate in planning and implementing Stage 5: enhanced monitoring in Grafana and future log aggregation solutions within AWS.

On-Premise Responsibilities:

• Maintain and develop automation using Puppet for infrastructure and platform provisioning.
• Collaborate closely with IGT's teams to align and implement changes in the on-premise environment.
• Engage in active discussions about architecture and future direction for on-premise solutions.
• Support and improve the on-premise Big Data stack as needed (e.g., Hadoop, Spark, Hive, Airflow, etc.).

Cross-Environment:

• Contribute to unified monitoring and observability solutions across AWS and On-Prem environments.
• Adapt to a broadening tech stack as cloud and on-prem requirements evolve.