Remote - Full-stack SAS (Statistical Analysis Software) Software Engineer

"ALL our jobs are US based and candidates must be in the US with valid US Work Authorization. Please apply on our website directly." US Citizen and Green Card only . Locals OnlyFull-stack SAS (Statistical Analysis Software) Software Engineer JA #1003216 Location: Dearborn, Mi hybridMAY consider someone remote in ESTAdditional Information: *POSITION IS HYBRID - WILL CONSIDER REMOTE FOR THE RIGHT CANDIDATE* Basic Software developer Hackerrank tests is required and scores. We prefer SAS Programmer Certification (Basic or advanced)Job DescriptionEnterprise Modernization team is seeking an experienced full-stack SAS (Statistical Analysis Software) Software Engineering professional familiar with Lean, Agile, and eXtreme Programming (XP) practices.You will be converting SAS programs from mainframe & server over to GCP for various application teams within Company.In addition, you will be developing SAS programs in SAS Mainframe, SAS Linux and SAS Viya (on GCP) platforms.You will work in a collaborative environment that leverages paired programming, while working on a small agile team to deliver Enterprise Modernization of migrating data to GCP.Work effectively with fellow software engineers, product owners, data champions and other technical experts.Demonstrate technical knowledge and communication skills with the ability to advocate for well-designed solutions.Skills RequiredExperience in Google Cloud Platform (especially BigQuery)SAS Viya 4.0programmingSAS 9.4 programming on Linux and Mainframe platformsMainframe experience (JCL, COBOL, CA7, DB2)Experience Required10+ years of professional development and administration experience with:Experience in Google Cloud Platform (especially BigQuery)SAS Viya 4.0 programmingSAS 9.4 programming on Linux and Mainframe platformsMainframe experience (JCL, COBOL, CA7, DB2)Experience using TeradataExperience writing Python and shell scripts on LinuxEducation RequiredBachelor's degree in computer science or a closely related scientific field of study