PDF Solutions
Lead DevOps Engineer
Dallas, Texas, USA · full-time
Sponsorship not specified$120k-$150kDetected 24 days ago
PythonJavaRubyPHPGitSQLNoSQLPostgreSQLMySQLMongoDBCassandraAWSAzureDockerKubernetesTerraformAnsibleGitHub ActionsJenkinsLinuxDevOpsMachine LearningData AnalysisAgile
About the role
- Overview PDF Solutions (NASDAQ: PDFS) offers an end-to-end analytics platform that empowers engineers and data scientists across the semiconductor ecosystem to rapidly improve the yield, quality, and profitability of their products. By combining industry-leading data analytics and professional services with exclusive, differentiated product data generated
- during the manufacturing process, PDF Solutions is delivering on the promise of Industry 4.0 today by transforming how the ecosystem collects, analyzes, and shares data. Key Fortune 500 organizations around the world rely on PDF Solutions to remove the data barriers that encumber and constrain new product introductions and to deliver the machine learning
Requirements
- Some domestic and international travel is required, usually less than 10% Good people and interactive skills
- Bachelor of Science in Computer Science, Information Technology or related engineering degree, or proven industry experience 4+ years' experience in IT with at least 1 year working with
Nice to have
- PDFS) offers an end-to-end analytics platform that empowers engineers and data scientists across the semiconductor ecosystem to rapidly improve the yield, quality, and profitability of their products.
- Headquartered in Santa Clara, California, PDF Solutions also operates worldwide in Canada, China, France, Germany, Italy, Japan, Korea, and Taiwan.
- Need Semiconductor industry experience.
- Help developers containerize their microservices and utilize orchestration tools like Kubernetes and Swarm to deploy and monitor these services.
- Provide operating, trend and statistical data reports as required.
Compensation
- $120k-$150k
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This listing is sourced directly from PDF Solutions's careers page and normalized into a canonical job model.