Senior Knowledge and Quality Specialist

CentralSquare Technologies•,

32d•Hybrid

About The Position

The Senior Knowledge & Quality Specialist owns knowledge creation and quality assurance for CentralSquare's customer support organization. This role drives what knowledge gets created, ensures it gets created on time, holds the support organization accountable for completing product-provided training, and makes sure QA is running consistently and producing actionable outcomes. This is not a passive role. The Senior Knowledge & Quality Specialist does not wait for KB articles to be written and then incorporate them into training — they drive the prioritization of what knowledge is needed, who is creating it, and when it will be done. They do the same for QA: defining the framework, ensuring audits happen on cadence, and making sure findings result in real behavior change. They are the connective tissue between what agents know, how agents are evaluated, and what the organization needs to improve. This role reports to the Director, CX Operations and works in close partnership with Support managers, the AI program team, and Product. Scope is pending final CPO alignment; the successful candidate will be informed of any adjustments prior to their start date.

Requirements

Own the knowledge creation pipeline: maintain a prioritized backlog of knowledge that needs to be created, updated, or retired based on case volume, deflection gaps, RCA findings, and agent feedback
Partner with Support managers to identify the most critical knowledge gaps and hold teams accountable for creating and reviewing content on time
Set and track SLAs for knowledge creation: articles should be drafted, reviewed, and published within defined timeframes; flag and escalate when those timelines slip
Ensure product-provided knowledge and release documentation is translated into agent-ready content in a timely manner; do not allow knowledge gaps to persist after product changes ship
Work with the AI program team to align KB article priorities: knowledge created for agent use should also feed AI deflection workflows, and vice versa
Maintain a knowledge health dashboard: coverage by product, article age, usage rates, and gaps identified through QA and support case patterns
Ensure agents complete product-provided training on time: own the tracking, reporting, and escalation process for training completion across the support organization
Partner with Support managers to build training completion into team operating rhythms; completion rates should be a managed metric, not an afterthought
Design and maintain onboarding programs for new support agents that reflect the current AI-assisted support environment
Develop supplemental training content where product-provided training is insufficient, outdated, or not agent-ready
Track training completion rates, knowledge assessment scores, and correlation with QA performance; use data to identify where training is working and where agents still have gaps
When new AI workflows or product changes ship, ensure agents are trained before they encounter the change in production — not after
Own the QA program: define evaluation frameworks, scoring rubrics, calibration processes, and audit cadences; ensure audits are happening on schedule, not just when time permits
Track QA throughput: how many interactions are being evaluated per agent, per team, per period; flag and escalate when cadence slips
Evolve QA rubrics as AI changes the case mix: as routine cases are deflected, human-handled interactions are harder and more complex, and QA standards must reflect that shift
Ensure QA findings are actioned: partner with Support managers to close the loop between evaluation results and coaching; a QA program that produces scores without behavior change is not working
Connect QA findings to the knowledge pipeline: recurring failure patterns on specific interaction types should trigger KB article creation or training content updates
Produce regular QA reporting for Support leadership: score trends by agent, team, product, and case type; distinguish systemic issues from individual coaching needs
Ensure QA rubrics account for AI-assisted agent interactions: how agents use suggested responses, when they override AI recommendations, and how they handle AI-to-human handoffs
Serve as the Knowledge & Quality voice in the AI program: ensure knowledge and QA requirements are factored into AI development specifications from the start, not retrofitted after launch
Partner with the Director, CX Operations to identify where agent knowledge gaps are driving poor AI escalation handling; translate those gaps into KB priorities and training initiatives
When AI capabilities change what agents do, own the knowledge and training response: update content, retrain affected teams, and update QA rubrics to reflect new expectations
Contribute Knowledge & Quality data to the unified support AI scorecard: KB coverage, training completion rates, knowledge assessment scores, and QA cadence metrics
This role does not have direct reports at launch; the Senior Knowledge & Quality Specialist owns QA and Knowledge functions directly
Build collaborative relationships with Support managers so that knowledge creation and QA are seen as shared priorities, not external mandates
Partner with Support managers to ensure QA is used as a development tool, not a surveillance mechanism

Responsibilities

Own the knowledge creation pipeline: maintain a prioritized backlog of knowledge that needs to be created, updated, or retired based on case volume, deflection gaps, RCA findings, and agent feedback
Partner with Support managers to identify the most critical knowledge gaps and hold teams accountable for creating and reviewing content on time
Set and track SLAs for knowledge creation: articles should be drafted, reviewed, and published within defined timeframes; flag and escalate when those timelines slip
Ensure product-provided knowledge and release documentation is translated into agent-ready content in a timely manner; do not allow knowledge gaps to persist after product changes ship
Work with the AI program team to align KB article priorities: knowledge created for agent use should also feed AI deflection workflows, and vice versa
Maintain a knowledge health dashboard: coverage by product, article age, usage rates, and gaps identified through QA and support case patterns
Ensure agents complete product-provided training on time: own the tracking, reporting, and escalation process for training completion across the support organization
Partner with Support managers to build training completion into team operating rhythms; completion rates should be a managed metric, not an afterthought
Design and maintain onboarding programs for new support agents that reflect the current AI-assisted support environment
Develop supplemental training content where product-provided training is insufficient, outdated, or not agent-ready
Track training completion rates, knowledge assessment scores, and correlation with QA performance; use data to identify where training is working and where agents still have gaps
When new AI workflows or product changes ship, ensure agents are trained before they encounter the change in production — not after
Own the QA program: define evaluation frameworks, scoring rubrics, calibration processes, and audit cadences; ensure audits are happening on schedule, not just when time permits
Track QA throughput: how many interactions are being evaluated per agent, per team, per period; flag and escalate when cadence slips
Evolve QA rubrics as AI changes the case mix: as routine cases are deflected, human-handled interactions are harder and more complex, and QA standards must reflect that shift
Ensure QA findings are actioned: partner with Support managers to close the loop between evaluation results and coaching; a QA program that produces scores without behavior change is not working
Connect QA findings to the knowledge pipeline: recurring failure patterns on specific interaction types should trigger KB article creation or training content updates
Produce regular QA reporting for Support leadership: score trends by agent, team, product, and case type; distinguish systemic issues from individual coaching needs
Ensure QA rubrics account for AI-assisted agent interactions: how agents use suggested responses, when they override AI recommendations, and how they handle AI-to-human handoffs
Serve as the Knowledge & Quality voice in the AI program: ensure knowledge and QA requirements are factored into AI development specifications from the start, not retrofitted after launch
Partner with the Director, CX Operations to identify where agent knowledge gaps are driving poor AI escalation handling; translate those gaps into KB priorities and training initiatives
When AI capabilities change what agents do, own the knowledge and training response: update content, retrain affected teams, and update QA rubrics to reflect new expectations
Contribute Knowledge & Quality data to the unified support AI scorecard: KB coverage, training completion rates, knowledge assessment scores, and QA cadence metrics
This role does not have direct reports at launch; the Senior Knowledge & Quality Specialist owns QA and Knowledge functions directly
Build collaborative relationships with Support managers so that knowledge creation and QA are seen as shared priorities, not external mandates
Partner with Support managers to ensure QA is used as a development tool, not a surveillance mechanism