BI Platform Lead Infrastructure Engineer

JPMorgan Chase & Co.•Columbus, OH

About The Position

Join a team where your infrastructure expertise directly shapes how thousands of employees access data and insights that drive business decisions across JPMorganChase. As a BI Platform Lead Infrastructure Engineer at JPMorganChase within Corporate Technology, you will serve as a critical pillar of operational excellence for enterprise Business Intelligence and analytics platforms. You will own the stability, performance, and continuous improvement of complex, multi-layered environments — bridging platform engineering, data teams, and business users to resolve incidents and prevent recurrence. In this role, you will also be at the forefront of integrating AI and large language model (LLM) capabilities into support workflows, helping the team work smarter, reduce toil, and deliver a better experience for every user who depends on these platforms.

Requirements

Formal training or certification on infrastructure engineering concepts and 5+ years applied experience
Demonstrated experience in platform support or operations for enterprise applications, within Business Intelligence, analytics, or data platforms
Working knowledge of at least one enterprise BI tool — Cognos and/or Tableau — in an administration or support capacity
Strong troubleshooting skills across logs, job failures, performance degradation, and access and authentication issues
Foundational Linux skills including process and service management, log inspection, and shell scripting basics
Experience working within IT Service Management (ITSM) processes and ticketing workflows, including incident, problem, and change management
Ability to communicate clearly and effectively with both technical engineering teams and non-technical business stakeholders
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.

Nice To Haves

Cognos administration or support experience, including scheduling, dispatchers, gateways, content store management, and permissions
Tableau Server or Tableau Cloud administration experience, including extract and refresh management, permissions, subscriptions, and backgrounder job troubleshooting
Hands-on experience with AI or LLM tools, including prompting, workflow integration, support automation use cases, or evaluation and accuracy assessment
Experience with monitoring and observability platforms such as Dynatrace, Splunk, or similar tools for alerting, log aggregation, and dashboard management
Scripting or automation experience using Python or Bash for operational tooling and workflow improvement
Familiarity with authentication and single sign-on concepts — including SAML, OAuth, or LDAP — as they relate to enterprise application access

Responsibilities

Troubleshoot complex platform issues spanning application, middleware, and infrastructure layers — including authentication, connectivity, scheduling, data extracts, permissions, and performance bottlenecks
Perform and oversee operational tasks including job and schedule monitoring, service restarts, log analysis, capacity assessments, and patch and upgrade support
Partner with engineering, infrastructure, and data teams to resolve high-impact incidents and drive permanent, root-cause-based fixes
Support Business Intelligence users with access requests, dataset and report issues, publishing, subscriptions, and dashboard troubleshooting — providing clear guidance on best practices for data refresh, report performance, and content governance
Identify and implement opportunities to automate repetitive support tasks — including triage, routing, known-issue matching, and user self-service — using AI and LLM-enabled workflows
Lead or contribute to pilots of LLM-powered support capabilities such as incident summarization, knowledge article drafting, smart alert correlation, and self-service assistants for common analytics questions
Establish lightweight governance frameworks for AI usage in support operations, including prompt standards, accuracy evaluation, and safe handling of sensitive data
Track and report on efficiency outcomes including mean time to resolution (MTTR) reduction, incident deflection rates, and repeat incident trends
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.