Electronic Arts - DataOps Developer III

• BS in Computer Science, Engineering, or a related field
• 7+ years of hands-on experience with DevOps or DataOps
• 6+ years of experience in Terraform, GitLab, SQL, Python, Airflow, Kubernetes, Docker, DBT, and Linux. (Must have)
• Experience in data warehousing technologies (Snowflake, BigQuery)
• Collaboration to work with diverse teams.
• Design secure, scalable, and supportable technical solutions.
• Experience troubleshooting and resolving.

We are a global team of creators, storytellers, technologists, experience originators, innovators and so much more. We believe amazing games and experiences start with teams as diverse as the players and communities we serve. At Electronic Arts, the only limit is your imagination.

As part of the Adrenaline team, you will join our Product Engineering team and lead projects that enhance the reliability of our data infrastructure. You will collaborate with multiple teams, including engineering, architects, analysts, project management, and other teams , to understand requirements and help shape the DataOps culture and practices within the organization, making a significant impact on outcomes.

You will report to Director of Data Architecture.

What you will do:

Responsibilities

• Work with EA DataOps and Operations teams to standardize processes and adopt industry best practices.
• Collaborate with partners, including engineers, analysts, architects and other partners to oversee data pipeline and features releases and deployments.
• Develop CI/CD pipelines and automation tools to ensure quick and reliable data deployment across multiple environments.
• Use tools such as Terraform, GitLab, SQL, and Python, along with technologies like Airflow, Kubernetes, Docker, DBT, and Linux.
• Ensure Service level agreements are met by managing alerts and responding to issues promptly, collaborating with other engineers or dependent teams.
• Troubleshoot deployment issues, performance problems, and data availability outages.
• Maintain comprehensive documentation of system configurations and procedures, including run books for live support.
• Improve data products performance through proactive troubleshooting and integrating best practices throughout the data development lifecycle.
• Evaluate and adopt new technologies to enhance team efficiency and platform capabilities.
• Provide guidance on managing end-to-end availability and performance of mission-critical data services, building automation to prevent problem recurrence, and creating automated responses for non-exceptional service conditions.
• Monitor the reliability and performance of data products and services in both production and non-production environments.
• Collaborate to develop a flexible architecture to promote engineering/developer self-service
• Develop automation coverage and targets of core build and deploy using cloud-native services and containers.
• Evaluate and adopt new technologies to enhance team efficiency and platform capabilities. (edited)