Enterprise Support Engineer

About the Business

LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management.

You can learn more about LexisNexis Risk at https://risk.lexisnexis.com/

About the Team

The LexisNexis Risk Solutions Product Support Engineering organization is a global, customer-focused team supporting mission-critical financial crime compliance platforms with the world’s top banking and financial institutions as our customers. The team partners closely with Product Experts, Product Tech and other cross-functional teams to protect service reliability, customer confidence, and continuous product improvement in fast-paced, SLA-driven environments.

About the Role

The Enterprise Support Engineer provides advanced technical support and engineering-level investigation into complex customer-reported production issues. The role combines end-to-end support case ownership with deep troubleshooting across the application, container platform, infrastructure, database, network, identity, messaging, storage, integration, and customer-configuration layers.

This is not a scripted triage position. The Enterprise Support Engineer gathers evidence, recreates customer scenarios, reproduces failures, identifies root causes, validates corrections, and communicates clear findings to customers and internal stakeholders. The engineer manages severity, escalations, and SLA commitments.

Responsibilities

• Own assigned customer support cases from intake through resolution, including accurate logging, prioritization, status updates, customer communication, and SLA adherence.
• Analyze application and container logs, platform events, configuration files, environment variables, secrets, data feeds, database records, API responses, deployment artifacts, and system behavior.
• Troubleshoot Kubernetes and OpenShift resources, including pods, deployments, services, ingress or routes, namespaces, ConfigMaps, secrets, persistent volumes, probes, resource limits, image versions, networking, and scaling behavior.
• Diagnose issues spanning Java web applications, REST APIs, databases, messaging middleware, object storage, authentication and SSO, TLS certificates, cloud infrastructure, and customer integrations.
• Distinguish among product defects, environmental issues, configuration errors, resource constraints, data problems, and integration failures using evidence-based root-cause analysis.