Lead Infrastructure Engineer

Lead Infrastructure EngineerAssume a vital position as a key member of a high-performing team that delivers infrastructure and performance excellence. Your role will be instrumental in shaping the future at one of the world's largest and most influential companies.As a Lead Infrastructure Engineer at JPMorganChase within the Corporate Sector – Chief Technology Office (CTO), you apply deep knowledge of software, applications, and technical processes within the infrastructure engineering discipline. Continue to evolve your technical and cross-functional knowledge outside of your aligned domain of expertise.Job ResponsibilitiesApplies technical expertise and problem-solving methodologies to projects of moderate scope and executes creative solutions for design, development, and technical troubleshooting for problems of moderate complexityApply strong technical judgment and structured problem-solving to deliver infrastructure changes end-to-end for BI platforms across Windows/Linux and AWS/SaaS environments.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.Lead a workstream spanning core infrastructure domains (cloud networking/connectivity, compute/storage, load balancing, DNS, certificates/TLS), managing scope, dependencies, risk, and status.Partner with security, IAM, network, SRE/operations, and vendors to architect and implement scalable, resilient solutions and platform modernization.Automate and standardize delivery using Infrastructure as Code and repeatable build/deploy practices.Troubleshoot moderately complex cross-domain issues (SSO/auth, network connectivity, OS/platform performance, application-to-data-source latency) and drive root cause to resolution.Evaluate upstream/downstream dependencies and deliver safe change plans (testing, phased rollout, backout) to minimize impact for a large user base.Build in security and compliance controls (least privilege, vulnerability remediation, encryption, audit-ready logging) and maintain runbooks/operational documentation.Contribute to an inclusive, respectful team culture through clear communication, collaboration, and knowledge sharing.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 skillsFormal training or certification on infrastructure engineering concepts and 5+ years applied experienceDeep knowledge of one or more areas of infrastructure engineering such as: hardware, networking terminology, databases, storage engineering, deployment practices, integration, automation, scaling, resilience or performance assessmentsDemonstrated 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.Deep knowledge of cloud infrastructure and multiple cloud technologies with the ability to operate in and migrate across public and private cloudsDeep knowledge of one specific infrastructure technology and scripting languages (e.g., Scripting, Python, etc.)Drives to continue to develop technical and cross-functional knowledge outside of the productDeep knowledge of cloud infrastructure and multiple cloud technologies with the ability to operate in and migrate across public and private clouds; Experience supporting enterprise-grade, third-party platforms in production, including vendor engagement, upgrades, and operational troubleshooting.Hands-on experience operating Windows and Linux environments (system services, logging, patching, access controls, and performance troubleshooting).Proficiency with infrastructure automation (Infrastructure as Code and scripting) and building repeatable deployment/operational patterns; Experience with observability and monitoring (metrics/logs/traces), alerting, and using telemetry to drive stability/performance improvements.Strong incident management skills, including clear communications during high-severity events and high-quality post-incident documentation with actionable remediation; Demonstrated ability to lead cross-team delivery: translating requirements into technical designs, managing dependencies, and driving execution in a controlled/change-managed environment.Preferred qualifications, capabilities, and skillsExperience supporting enterprise Qlik Sense environments at scale (high concurrency / large user base), including node/service operations, monitoring, upgrades, reload scheduling/publishing, and operational troubleshooting.Experience administering and troubleshooting BI platforms such as Tableau, ThoughtSpot, or Sigma, including user access patterns, performance tuning, and integration with data sources. Experience integrating BI tools with enterprise identity and SSO (e.g., Active Directory fundamentals, SAML/OIDC concepts), including certificate/TLS management and authentication troubleshooting.Experience supporting third-party SaaS BI products hosted on AWS, including vendor incident coordination/escalation, service health verification, and network/connectivity considerations.Familiarity with AWS fundamentals relevant to SaaS and hybrid integrations (e.g., IAM, VPC networking, security groups, load balancing, DNS, and monitoring primitives). Strong Windows/Linux operational skills for BI tooling and gateways/connectors (service troubleshooting, log-based diagnosis, patching, and performance analysis). Experience operating in regulated or audit-controlled environments with strong change controls, documentation, and traceability.Ability to drive cross-team reliability improvements using telemetry (monitoring/alerting, SLOs/SLIs) and post-incident learnings to reduce recurrence.