QA Automation Engineer
Full-time · 12 monthsOwns manual discovery and validation, QA process setup, documentation, test data, automation, and maintenance.
QualityWorks Consulting Group · Project proposal
Quality
engineering
for APQC
A dedicated QA Automation Engineer and QualityWatcherAI, one combined 12-month quality workstream within Tag1's modernization plan.
For Tag1 · APQC engagement
Engagement Quality Engineering Enablement
Date July 2026
Our recommendation
One person and one platform create APQC's quality function.
Automation-first. Human-led. Built to become sustainable.
QualityWatcherAI
License and enablement · 12 monthsAccelerates test creation, repeatable execution, evidence, and shared quality knowledge.
Quality Engineering Lead
Fractional · Year oneProvides strategy, architecture, governance, and alignment within Tag1's delivery model.
RecommendedThe engineer creates context and maintains coverage; QualityWatcherAI scales execution, evidence, and shared knowledge.
Who we are
A decade of quality engineering, across the region and beyond.
Since 2010, QualityWorks has helped teams improve release confidence through quality strategy, test automation, DevOps alignment, AI-assisted testing, and practical enablement.
Quality strategy
Risk, coverage, testability, and release evidence.
Automation engineering
Reliable API, integration, and browser coverage.
AI-assisted testing
Faster knowledge capture, authoring, and execution.
Team enablement
Runbooks, training, governance, and ownership transfer.
Our established Tag1 relationship lets this quality workstream join the existing cadence without creating a second delivery system.
Current state
Automation-first starts by understanding the system.
Documentation exists for some Tag1-delivered work, but its completeness across APQC's estate is still unknown.
Missing documentation changes the first tasks, not the automation goal.
Human + AI
The engineer creates context. QualityWatcherAI creates scale.
CapabilityQA Automation EngineerQualityWatcherAI
Knowledge and requirementsLeads manual discovery, documents workflows, maps risk, and confirms expected behaviorGenerates structured tests and organizes approved project context
New or changing featuresManually explores, validates, and determines riskRepeats known checks consistently
AutomationDesigns, integrates, and maintains the suitesAccelerates authoring, version review, and execution
Failures and evidenceDiagnoses, triages, and decides the responseCaptures results, screenshots, video, traces, and logs
OwnershipCreates runbooks, trains teams, and interprets riskCentralizes tests, history, and team access
Combined outcomeAccountable human judgment plus persistent platform capability.
The 12-month plan
One workstream moves from discovery to scaled automation.
Discover + configure
Audit evidence, map critical journeys, capture knowledge, establish QA practices, and configure QualityWatcherAI.
Pilot
Automate stable, high-value journeys and connect execution to Tag1's delivery workflow.
Scale
Expand regression coverage, release evidence, integration checks, and platform usage.
Optimize + transfer
Harden suites, complete runbooks, train owners, and define the year-two operating model.
Automation strategyStart immediately on stable, understood journeys; expand as environments, data, and expected outcomes mature.
Year-one scope
Year one delivers a working quality system, not only test scripts.
Coverage model
Critical-journey inventory, risk map, and prioritized regression backlog.
Living test knowledge
Approved requirements, test artifacts, context, and traceability.
Automated suites
Stable smoke and regression coverage for agreed high-value journeys.
Configured platform
QualityWatcherAI projects, environments, automation, and execution history.
Release evidence
Quality checks, defect evidence, and release-readiness reporting.
Ownership transfer
Runbooks, training, maintenance guidance, and year-two recommendations.
What changes for APQC
The investment turns quality work into measurable business value.
AreaTodayYear-one outcomeBusiness value
Release validationSporadic or assembled lateRepeatable release evidence supporting a predictable cadenceFaster, clearer release decisions
RegressionManual, inconsistent, or limitedScheduled automation, progressing toward nightly runs for stable critical journeysLess manual effort and earlier feedback
KnowledgeConcentrated in key personnelLiving workflows, test cases, test data, and runbooksFewer bottlenecks and less dependency
Defect detectionIssues found late in the cycleEarlier, repeatable checks with tracked escaped defectsLess rework and lower production risk
Quality reportingLimited shared evidenceConsistent coverage, execution, and defect reportingExecutive visibility into release readiness
First 60 daysEstablish the baseline, then report release cadence, regression time, staff hours returned, coverage, and escaped defects.
Proposed year-one investment
A $147,600 investment in repeatable QA and release confidence.
QA Automation Engineer
$105,600$8,800 per month for discovery, validation, automation, maintenance, and transfer.
QualityWatcherAI
$42,000$30,000 annual license plus $12,000 setup, support, and enablement.
Year-one total
$147,600Fractional Quality Engineering leadership and architectural oversight included.
Reduce repeated manual validation by key personnel.
Find issues earlier and reduce rework and production incidents.
Support more frequent releases without scaling manual QA effort at the same rate.
Realized ROI will be measured using the APQC baseline established during discovery.
Decision and next steps
Three steps launch the combined model.
Align
Confirm priority workflows, success measures, and the initial automation scope.
Confirm
Finalize access, security, commercials, start date, and the QualityWatcherAI package.
Launch
Onboard the engineer, configure QualityWatcherAI, and begin the 60-day discovery and baseline period.
Decision requestedApprove one full-time QA Automation Engineer plus one year of QualityWatcherAI.