About the role :
As a Scaled Customer Engineer, you will work with the Business team to introduce Google Cloud to our customers. You will help customers and partners understand the benefits of Google Cloud, develop creative cloud solutions and architectures to solve their business challenges, and solve potential technical roadblocks/challenges.
- Support business teams in pursuit of opportunities and engage customers to address aspects of their on-premise and/or cloud infrastructure, applications deployment, and data lifecycle.
- Identify business and technical requirements, conduct full technical discovery, and architect client solutions to meet gathered requirements.
- Lead technical projects, including technology advocacy, product and solution briefings, proof-of-concept, the coordination of supporting technical resources, and more.
- Work with Google Cloud Platform products to demonstrate and prototype integrations in customer/partner environments.
- Prepare and deliver product messaging to showcase the Google Cloud Platform value proposition, using techniques including slide presentations, product demonstrations, and more.
Candidate requirements :
- Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
- 2 years of experience in virtualization or cloud native architectures in a customer-facing or support role.
- Experience with cloud technologies and architectures across cloud tools, SaaS, PaaS, and IaaS.
- Experience in performing technical presentations or public speaking.
- Master’s degree in Computer Science or in a related technical field.
- Experience with technical sales or consulting in cloud computing, data, networking, information lifecycle management, or big data.
- Understanding of DNS, TCP, firewalls, proxy servers, load balancing, VPN, VPC, and working knowledge of Linux.
- Familiarity with architecture and operational aspects of large scale distributed systems, including technologies such as Kubernetes, Istio, etc.
- Familiarity with the popular technologies in the machine learning/artificial intelligence ecosystem (e.g., Tensorflow, Spark, Kubeflow, etc.)