Lead Infrastructure Engineer – Risk & BI Platforms, AI-Champion

Join us to modernize business intelligence platform operations with automation and AI-driven solutions, advancing your career in a high-impact engineering role. You will help shape resilient, compliant, and innovative technology environments.

As a Lead Infrastructure Engineer at JPMorganChase within the Chief Technology Office team, you will own availability and resiliency outcomes for a portfolio of on-premises and SaaS applications, focusing on business intelligence platforms. You will proactively reduce operational breaks through automation and AI-enabled workflows, ensuring audit-ready compliance with firmwide controls and standards. You will collaborate with engineering, infrastructure, security, and vendor teams to deliver platform stability and lifecycle management. You will help maintain a culture of operational excellence and risk stewardship.

Job responsibilities

• Own stability, performance, lifecycle, and operational readiness for assigned platforms and services across on-premises and SaaS environments
• Lead incident triage, root cause analysis, corrective actions, and prevention of repeat issues through automation and standardization
• Partner with engineering, infrastructure, security, and vendor teams to manage upgrades, patching, certificates, integrations, and connectivity
• Provide hands-on platform support and engineering for BI tools such as Tableau, IBM Cognos, SAP BusinessObjects, ThoughtSpot, and Qlik Sense
• Engineer and operate platform components including capacity/scaling, authentication integrations, certificate management, tech upgrades, monitoring/alerting, and environment hygiene
• Build and maintain Infrastructure as Code and automation for provisioning, configuration, compliance checks, health checks, self-healing, and reporting
• Implement and evolve CI/CD and operational pipelines to improve speed and safety of changes
• Improve observability via metrics, logs, traces, meaningful SLOs/SLIs, alert quality, and runbooks
• Uses enterprise-authorized AI capabilities within the work environment to accelerate infrastructure analysis and design documentation, validating outputs and handling operational data according to sensitivity and security requirements. 
• Applies reuse-first, AI-assisted practices within delivery and automation routines to identify recurring issues and validate remediation options, ensuring changes are traceable/auditable and aligned to resiliency and security expectations. 

Required qualifications, capabilities and skills

• Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience)
• 8+ years in site reliability engineering, DevOps, platform engineering, infrastructure automation, or production engineering roles
• Strong proficiency in Python, Bash, PowerShell, or Go
• Hands-on experience with CI/CD tools such as Jenkins, Spinnaker, or GitLab CI
• Hands-on experience with Infrastructure as Code tools such as Terraform and Ansible
• Strong experience with monitoring and observability tools such as Dynatrace, Splunk, Grafana, or Datadog
• Experience with cloud platforms including AWS, Azure, or GCP
• 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. 
• Experience with secrets management, least-privilege access, and security best practices
• Hands-on experience supporting infrastructure platform and SRE for BI tools such as SAP BusinessObjects, ThoughtSpot, Tableau, Qlik Sense, and IBM Cognos

Preferred qualifications, capabilities and skills

• Experience with Snowflake, Databricks, or other cloud data platforms
• Knowledge of SDLC processes, Agile/Scrum methodologies, and change management
• Experience with enterprise schedulers such as Autosys, Control-M, or similar
• Experience with chaos engineering, resiliency patterns, or reliability engineering practices at scale