• Develop and provide cloud computing services (platform-as-a-service or infrastructure-as-a-service) suggestions/proposals for SME, Enterprise and various industry verticals.
• Design data solutions aligned to the design principles and security standards of the Google Cloud Platform.
• Design and implement solutions around data warehouse implementation ranging from architecture, ETL processes, multidimensional modelling, data marts implementation.
• Design and optimize data acquisition, data ingestion, data processing using ETL (Extract, Load, Transform) tool and developing dashboards or other visualizations using Google Cloud Platform or other solutions.
• Evaluate the requirements and determine actionable tasks, estimates and provide efforts for business solution build and architecture.
• Responsible for technical documentation on solution implementation.
• Perform operational readiness tasks and ensure production acceptance criteria are met.
• Respond to platform technical issues in a professional and timely manner.
• Work closely with the sales team for cloud-related sales opportunities.
• Support Pre-Sales activities, i.e. proposal, systems design, proof-of-concept, demo, workshop conduct, etc. Requirements:
• Bachelor’s degree in Computer Science, related technical field, or equivalent practical experience.
• 3 – 5 years of experience in Business Analytics, Data Engineering or Data Science.
• Strong logical/analytical troubleshooting.
• Experience in deploying and operating multi/hybrid-cloud platforms, such as Kubernetes, Anthos etc is a plus
• Working closely with multiple teams (Engineering, Security, Operations & Development teams) to maintain, deploy and operate containerized solutions
• Work with computer engineers to integrate software systems • Hold hands on role that consists of administrating Mirantis(Former Docker Enterprise) based Kubernetes cluster, containerizing apps
• Spreading the usage of containers in the company through automation and coaching
• Deploy and configure solutions in the cloud.
• Managing Kubernetes platform
• Automating the provisioning of environments
• Troubleshooting incidents / problems
• Thriving for continuous improvement of the production environment
• Redesigning legacy applications for container implementation
• Assist on DR, BCP exercise and patching cycles
