Principal Software QA Engineer

Principal Software QA Engineer responsible for ensuring accuracy, reliability, and quality of E&P data, applications, and workflows across subsurface and drilling domains. Works with cloud‑based deployments, automation, and containerized environments to validate datasets, develop automated QA scripts, support AWS/Azure deployments, build and test Docker images, manage Kubernetes workloads, and participate in CI/CD processes. Collaborates with multidisciplinary teams to maintain compliance with API, ISO and internal standards and provide QA insights during system upgrades and migrations.

• Perform QA for Landmark applications, ensuring data integrity and workflow consistency
• Validate subsurface, drilling, and production datasets for completeness, accuracy, and compliance
• Develop and maintain automated QA scripts using scripting and automation tools
• Support cloud‑based E&P application deployments on AWS and Azure
• Build, test and validate Docker images and container configurations
• Deploy and troubleshoot Kubernetes workloads for E&P applications
• Participate in CI/CD processes, automated testing, documentation updates and release validation
• Conduct root cause analysis for data or application quality issues
• Collaborate with subsurface, drilling, IT and DevOps teams to ensure digital workflows meet quality standards
• Document QA processes, test cases, and validation results
• Ensure compliance with API, ISO and internal quality standards
• Provide technical QA insights during system upgrades, migrations and new tool deployments
• Utilize AI tools to improve testing efficiency

• Strong experience with OpenWorks, Petrel, and Landmark EDT applications
• Deep understanding of E&P data types: seismic, well logs, drilling and production data
• Proficiency in Linux and Windows scripting (Bash, PowerShell, Python)
• Experience with AWS and Azure cloud services (compute, storage, IAM, networking)
• Strong knowledge of Docker and container lifecycle management
• Experience deploying and troubleshooting Kubernetes clusters
• Familiarity with Git, CI/CD pipelines, and automated testing frameworks
• Understanding of data governance, lineage, and quality rules for subsurface and drilling datasets