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