Remote Senior Streaming Engineer

Summary

The job description is for a Senior Big Data Engineer role at Rackspace Technology. The role involves working remotely and requires strong skills in developing and scaling both stream and batch processing systems, particularly with GCP. The ideal candidate should have experience in Java and Python, and hold the Google Associate Cloud Engineer Certification or other Google Cloud Professional level certification.

Requirements

• Experience in building a reusable, and reliable code for stream and batch processing systems at scale
• Expertise in public cloud services, particularly in GCP
• Experience with GCP managed services and understanding of cloud-based messaging/stream processing systems are critical
• Utilize tools for continuous integration and continuous deployment (CI/CD), and Infrastructure as Code (IaC) like Terraform to automate and improve development and release processes
• Knowledge in containerization technologies such as Docker and Kubernetes to enhance the scalability and efficiency of applications
• Ability to tackle complex challenges and devise effective solutions
• Worked effectively in a remote setting, maintaining strong written and verbal communication skills
• Proven experience in engineering stream/batch processing systems at scale
• Strong programming abilities in Java and Python
• Hands-on experience in public cloud platforms, particularly GCP. Additional experience with other cloud technologies is advantageous
• Strong Java 11-17 experience
• Dataflow (Managed Apache Beam)
• Pub/Sub
• Google Associate Cloud Engineer Certification or other Google Cloud Professional level certification
• 4+ years of experience in customer-facing software/technology or consulting
• 4+ years of experience with ‘on-premises to cloud’ migrations or IT transformations
• 4+ years of experience building, and operating solutions built on GCP (ideally) or AWS/Azure
• Technical degree: Computer Science, software engineering or related

Responsibilities

Build a reusable, and reliable code for stream and batch processing systems at scale