• 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
• 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.
• 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
• 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
• Hands-on experience with AWS/Azure Cloud platforms and Python or other scripting tools
• Hands-on with Git, CI/CD pipelines, and DevOps deployment practices. Experience with reporting and dashboarding using at least one data visualization platform (e.g., Tableau). Experience with CMDB-type inventory management and reporting tools such as ServiceNow and with any observability platforms such as Splunk, Grafana, Dynatrace.
• Use Agile practices and backlog & prioritization tools such as Jira to plan, build and deliver the work. Turning Manual into automated workflows on a continuous basis
• Knowledge of how infrastructure assets (Cloud, on-prem, private Cloud, SaaS)are registered, stored, and tracked through end-to-end life cycle. Ability to shift from reactive to proactive methodologies to remediate and mitigate security risks. Strong background in Tech Risk & Controls oversight, monitoring, and reporting, with proven ability to execute controls framework activities in partnership with technology and risk stakeholders
• Understanding of ITAM lifecycle governance and how asset inventory and classification impact control effectiveness and findings
• 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.