VP M&A E2E VP Quality Engineering

LPL Financial•San Diego, SC

About The Position

The E2E QE Lead, reporting to the SVP, Technology with a dotted line to the AI Lead, owns end-to-end quality governance for the Equitable migration. This role sets the standards and oversees their execution — it does not perform the testing itself: application-level and end-to-end test execution is delivered by a Centralized Domain App & QE cell, and SMEs own UAT sign-off. The Lead defines SME-ready workbook criteria and AI-PR acceptance criteria, owns the functional phase Go/No-Go quality checklist, runs weekly triage with Remediation Operations (RemOps), and owns the quality metrics that tell the program whether it is on track — false-positive rate, PR merge rate, and CI failure taxonomy. The role sets the bar and oversees the bar; it is distinct from the testing cell that executes against it, and from the E2E Solution Lead role.

Requirements

Mimimum of 8 years in quality engineering/assurance, including a mimimum of 3 years leading quality and test strategy for complex, multi-team software programs (governance and oversight, not hands-on test execution).
Demonstrated ownership of end-to-end quality governance — acceptance criteria, phase Go/No-Go gates, and quality bars — with measurable metrics such as defect-escape rate, false-positive rate, and test coverage.
Experience with test-management and defect-tracking tooling (e.g., qTest and Jira) and structured test-environment and UAT governance.
Experience governing vendor/partner test execution and coordinating SME-owned UAT sign-off across multiple domains.
Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience), with a mMimimum of 8+ years in quality engineering/assurance including leadership of quality governance on enterprise programs.
Defines clear, objective quality criteria that others can execute and be measured against.
Makes disciplined, evidence-based Go/No-Go calls under schedule pressure.
Manages quality through metrics and trends, not anecdotes.
Drives quality outcomes through governance, partnership, and oversight across LPL and vendor teams.

Nice To Haves

Experience governing quality for AI/ML-generated artifacts or automated remediation pipelines.
Familiarity with Testing tools, Jira, and structured test-environment and UAT governance.
Background in regulated financial-services migrations or large platform-consolidation programs.
Experience defining acceptance criteria at the boundary between automated CI and human UAT sign-off.

Responsibilities

Define and maintain the criteria that make an SME-review workbook ready — completeness, clarity, and traceability — and audit workbooks against them each cycle.
Own the acceptance criteria for AI-generated pull requests, partnering with the Applied AI Engineer on quality gates and disposition requirements.
Own the functional Go/No-Go quality checklist for each migration phase and hold the quality line at phase gates.
Run weekly quality triage with Remediation Operations to review failures, escapes, and emerging risks.
Define, track, and report the program’s core quality metrics — false-positive rate, PR merge rate, and CI failure taxonomy — to leadership and governance.
Set and oversee the application-level and end-to-end test bar executed by the Cognizant Domain App & E2E QE cell, and ensure SME-owned UAT sign-off is well-defined and respected.
Coordinate quality expectations across the engine, DB & App, batch, and SME tracks, and integrate into the program test strategy.
Establish defect-escape and quality thresholds that feed the cutover Go/No-Go decision, escalating risks early.