GCP Cloud Architect - Technical Architect- Springfield

Location: Springfield, MO 

Job Type: Full-Time

About Us: We are a leading technology solutions provider, committed to delivering innovative and scalable cloud solutions. We are looking for a highly skilled and experienced GCP Architect to join our team and lead our cloud foundation build, on-premises GCP migration, and Vertex AI implementation projects.

Job Description:

Responsibilities:

• Design and implement robust cloud architectures on Google Cloud Platform (GCP).
• Lead the cloud foundation build, ensuring best practices in security, scalability, and performance.
• Manage and execute the migration of on-premises infrastructure to GCP.
• Implement and optimize Vertex AI solutions for advanced machine learning and AI capabilities.
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
• Provide technical leadership and mentorship to junior engineers and architects.
• Ensure compliance with industry standards and regulatory requirements.
• Troubleshoot and resolve complex technical issues related to GCP infrastructure and services.
• Stay updated with the latest GCP features, tools, and best practices.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
• Minimum of 5 years of hands-on experience with GCP architecture and services.
• Proven experience in cloud foundation build and on-premises to GCP migration projects.
• Strong expertise in Vertex AI and implementing machine learning models on GCP.
• Proficiency in GCP services such as Compute Engine, Cloud Storage, BigQuery, Cloud Functions, and Kubernetes Engine.
• Excellent understanding of networking, security, and IAM in GCP.
• Strong problem-solving skills and the ability to work in a fast-paced environment.
• Excellent communication and collaboration skills.

Technical Skills Required:

• Experience with Terraform, Ansible, or other infrastructure as code (IaC) tools.
• Knowledge of DevOps practices and CI/CD pipelines.
• Proficiency in scripting languages such as Python, Bash, or PowerShell.
• Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
• Familiarity with monitoring and logging tools such as Stackdriver, Prometheus, and Grafana.
• Understanding of data engineering concepts and tools like Dataflow, Dataproc, and Pub/Sub.
• Experience with API management and microservices architecture.
• Knowledge of security best practices, including encryption, key management, and identity management.
• Familiarity with other cloud platforms like AWS or Azure.
• Experience with cloud-native application development and serverless architectures.
• Proficiency in designing and implementing disaster recovery and business continuity plans.
• Knowledge of cloud cost management and optimization strategies.
• Experience with hybrid cloud environments and multi-cloud strategies.
• Familiarity with cloud compliance frameworks such as GDPR, HIPAA, and SOC 2.
• Proficiency in using GCP's AI and machine learning tools, such as AutoML and AI Platform.
• Experience with data warehousing solutions like BigQuery and data lake architectures.
• Knowledge of cloud networking concepts, including VPC, VPN, and interconnects.

Preferred Skills:

• GCP Professional Cloud Architect certification.
• Experience with hybrid cloud environments and multi-cloud strategies.
• Knowledge of machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.

Experience with serverless computing and event-driven architectures

 

Key performance indicators (KPIs) for a GCP Architect can help measure the effectiveness and success of their work. Here are some important KPIs to consider:

Cloud Infrastructure Uptime: Measure the availability and reliability of the cloud infrastructure. High uptime indicates a stable and well-maintained environment.

Cost Optimization: Track cloud spending and identify opportunities for cost savings. This includes monitoring resource utilization and implementing cost-saving measures.

Migration Success Rate: Evaluate the success of on-premises to GCP migration projects. This includes the percentage of successful migrations completed on time and within budget.

Performance Metrics: Monitor the performance of cloud applications and services, including response times, latency, and throughput. Ensure that performance meets or exceeds predefined SLAs.<