• Applies technical knowledge and problem-solving methodologies to projects of moderate scope, with a focus on improving the data and systems running at scale, and ensures end to end monitoring of applications
• Resolves most nuances and determines appropriate escalation path
• Executes conventional approaches to build or break down technical problems
• Drives the daily activities supporting the standard capacity process applications
• Partners with application and infrastructure teams to identify potential capacity risks and govern remediation statuses
• Considers upstream/downstream data and systems or technical implications
• Be accountable for making significant decisions for a project consisting of multiple technologies and applications
• Uses enterprise-authorized AI capabilities within the work environment to accelerate monitoring and capacity analysis and documentation, validating outputs and handling operational data according to sensitivity and security requirements.
• Applies reuse-first, AI-assisted approaches to identify recurring capacity risks and improve remediation workflows, ensuring changes are validated and aligned to resiliency and security expectations.

Required qualifications, capabilities, and skills

• Formal training or certification on infrastructure engineering concepts and 3+ years applied experience
• Strong knowledge of one or more infrastructure disciplines such as hardware, networking terminology, databases, storage engineering, deployment practices, integration, automation, scaling, resilience, and performance assessments
• Strong knowledge of one or more scripting languages (e.g., Scripting, Python, etc.)
• Experience with multiple cloud technologies with the ability to operate in and migrate across public and private clouds
• Drives to develop infrastructure engineering knowledge of additional domains, data fluency, and automation knowledge
• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support infrastructure engineering workflows with strong validation habits and awareness of data sensitivity.
• Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations.