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