L3 Support Engineer

Job Summary

The L3 Support Engineer is the deep technical investigation specialist within the support organization. This role exists to permanently eliminate the root causes of recurring tickets, not to process individual incidents. L3 engineers work on the hardest problems: payroll back-calculation failures, integration breakdowns, compliance regulation misconfigurations, deployment-caused regressions, and cross-system issues that span configuration, product, and infrastructure layers. L3 engineers do not own client accounts or manage queues. They receive escalations from Support Engineers, investigate to root cause, coordinate fixes with Product, Engineering, Compliance, and Configuration teams, and close the loop by documenting findings that feed both the knowledge base and AI training data. Their success metric is not tickets resolved — it is recurring issues permanently eliminated.

Duties & Responsibilities

Deep Technical Investigation

• Investigate complex L3 escalations: payroll calculation failures, compliance regulation errors, integration file discrepancies, and product defects
• Perform root cause analysis using back-calculation methodology, regulation tracing, configuration audits, and interface file analysis
• Determine whether root cause is configuration, compliance, product, or infrastructure
• Validate deployment changelogs and release notes for potential regression risks; conduct post-deployment spot checks on high-risk configurations

Cross-Functional Coordination

• Coordinate resolution with functional teams: create dev tasks for Product/Engineering, file compliance corrections, work with Configuration on entity-level fixes
• Track every L3 investigation to permanent closure: verify fixes in production, confirm no recurrence
• Participate in compliance training sessions as subject matter expert; share back-calculation techniques with Support Engineers

Knowledge & AI Contribution

• Document all findings in structured format for the knowledge base: root cause, resolution steps, affected configurations, prevention measures
• Work with the AI Operations Lead to convert L3 patterns into AI training data, enabling AI to detect and flag similar issues proactively
• Maintain a root cause register: a living document tracking all identified root causes, their status, and the ticket clusters they affect

Coverage

• Provide L3 on-call coverage for P1 emergencies requiring deep technical investigation outside business hours
• Operate primarily during core business hours (09:00–18:00 CET) when cross-functional teams are available

Skills & Qualifications

Required Competencies

• Demonstrated expertise in root cause analysis methodologies; able to trace complex, multi-system issues through multiple layers (UI, API, configuration, regulation, database)
• Understanding of gross-to-net payroll calculations, statutory compliance requirements, back-calculation methodology, and country-specific regulation steps
• Comfortable reading and analyzing logs, database queries, API payloads, and interface files
• Strong technical writing; able to produce clear root cause reports and KB articles usable by both humans and AI systems
• Effective cross-team collaboration; able to drive accountability for fixes through influence and clear documentation
• Experience with dev task creation, CI/CD pipeline awareness, and deployment impact analysis

Experience & Education

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent technical field. Payroll/compliance certifications a strong plus
• Minimum 3 years in L3/Tier 3 technical support, application engineering, or payroll system implementation
• Deep experience with at least one of: payroll regulation/compliance, HCM system configuration, or integration engineering
• Multi-country payroll experience highly preferred

SMART Goals

Root Cause Elimination

• Specific: Permanently eliminate the top recurring root causes across all clients
• Measurable: Number of root causes fully resolved and verified in production
• Achievable: Through systematic investigation, cross-functional coordination, and production verification
• Relevant: Directly reduces ticket creation volume at the source
• Time-bound: 15 root causes permanently closed within 90 days

L3 Backlog Clearance

• Specific: Reduce the L3-eligible ticket backlog to zero aged tickets
• Measurable: Number of L3 tickets older than 10 business days
• Achievable: Through focused investigation prioritized by age and client impact
• Relevant: Prevents chronic backlog that undermines SLA and client confidence
• Time-bound: Zero aged L3 tickets within 60 days

Root Cause Register

• Specific: Establish and maintain a living root cause register tracking all identified root causes, their status, and affected ticket clusters
• Measurable: Register operational and reviewed in weekly meetings
• Achievable: Using existing ticket data and investigation findings
• Relevant: Provides visibility into systemic issues and tracks progress toward elimination
• Time-bound: Register operational within 30 days

KB Documentation Rate

• Specific: Document 100% of L3 closures with full knowledge base articles including root cause, resolution, and prevention
• Measurable: Percentage of L3 closures with complete KB documentation
• Achievable: By integrating documentation into the resolution workflow
• Relevant: Feeds AI learning pipeline and enables Support Engineers to handle similar issues independently
• Time-bound: 100%, ongoing from day 1

AI Training Data Contribution

• Specific: Convert all L3 root cause patterns into structured AI training data
• Measurable: Percentage of L3 closures fed into the AI learning pipeline via the AI Operations Lead
• Achievable: Working with the AI Ops Lead to structure findings into machine-readable patterns
• Relevant: Creates the flywheel where L3 human expertise amplifies AI capability
• Time-bound: 100% of closures fed to AI pipeline, ongoing from day 30

Post-Deployment Validation

• Specific: Conduct spot checks on high-risk client configurations after every deployment
• Measurable: Percentage of deployments validated within 4 hours
• Achievable: Using changelog analysis and automated regression check scripts
• Relevant: Prevents deployment-caused escalation spikes (Q1 2026: deployments doubled ticket counts)
• Time-bound: 100% of deployments checked within 4 hours, starting from day 30

Deployment Escalation Reduction

• Specific: Reduce deployment-related escalation tickets by 50%
• Measurable: Escalation tickets traced to deployment regressions vs. Q1 2026 baseline
• Achievable: Through post-deployment validation, proactive regression detection, and improved QA feedback loops
• Relevant: Deployments are the single biggest driver of support overload
• Time-bound: 50% reduction within 90 days