Lead hybrid-cloud reliability and component delivery for cashless payments, manage a team, and advance infrastructure, operations automation and IT improvements.
About this role
Design, implement and maintain networks for a hybrid-cloud platform supporting in-person cashless payments. The role handles internal and external connectivity and the demanding security requirements of payment infrastructure. It identifies opportunities to improve systems and operations, proposes changes proactively and works with colleagues across departments.
Applicants need at least three years as a network engineer, with firewall design and implementation, network-device design and implementation, and network design and implementation in a hybrid-cloud environment. They also need three or more years of infrastructure experience in at least one of Linux, Windows, AWS, Azure or middleware. This is an additional requirement, not a demand for experience in every listed technology. Certifications and mission-critical operations experience are preferred.
This is an AI-generated summary of the employer's original posting — details can be incomplete, out of date or simply wrong. Always confirm everything on the official posting before applying.
Similar jobs
Lead payment-platform application delivery from requirements through release, develop new services and business workflows, manage a team and drive IT improvements.
Support data center operations by tracking and escalating problem tickets, managing maintenance windows and changes, and applying Linux, networking, and scripting skills.
Manage customer solutions for AWS automotive and manufacturing customers, using cloud implementation, migration, modernization and technology problem-solving experience.
Senior Engagement Manager for large-scale system projects, requiring project leadership, professional AWS certification, AI-assisted migration, core-system cloud migration, and AWS modernization.
Business Analyst focused on logistics and last-mile delivery analytics, with work involving AWS data environments, ETL, data modeling, statistical and A/B testing, machine learning, and AI tools.