Intelligence Engineer: Quality Expert and Outcome Validator

This role is not about writing test cases, executing regression suites, or validating features after the fact.
We are looking for someone who treats quality as a system, not a checklist. Your job is to detect risk early, surface patterns in failures, and help engineering teams prevent issues before they reach production.
If your background is purely manual QA, UAT, or test execution, this role will not be a fit.
What You’ll Actually Do
Analyze production, staging, and test data to identify quality patterns, anomalies, and early warning signals
Design quality metrics and indicators that reflect system health, not just pass/fail results
Investigate recurring defects and failures to identify systemic root causes
Partner closely with engineers to:
validate assumptions
challenge incomplete fixes
prevent regressions at the system level
Build lightweight tooling, queries, or dashboards to make quality visible and actionable
Use data to answer questions like:
“Where are we accumulating hidden risk?”
“Which parts of the system are becoming fragile?”
“What is likely to break next, and why?”
What This Role Is Not
Not a manual QA role
Not a test execution role
Not a UAT sign-off role
Not a checklist or compliance-only QA role
What We’re Looking For
Strong analytical and systems thinking
Comfort working with data (queries, logs, metrics, trends)
Ability to reason about cause and effect in complex systems
Engineering mindset, even if you’re not a full-time developer
Willingness to challenge assumptions and ask uncomfortable questions
Helpful Backgrounds (but not required)
Software engineering
Data engineering / analytics
SRE / production support
Platform or infrastructure engineering
Semiconductor, manufacturing, or systems engineering with strong data exposure
Explicit Note
If your primary experience is manual testing, writing test cases, or executing predefined QA scripts, this role is not a match.