Head of QA Engineering

About the Role

Our Product and Engineering team runs four platforms: SPARK (our AI platform for TPA operations), AGL (Advisor Growth Lab, our advisor community), Nexus Bridge (our systems integration layer), and Strongpay (our payroll and HR platform for small and mid-sized businesses, with thousands of logins every weekday). All four ship at the end of every two-week sprint.

Our engineers maintain unit and integration test suites and run them before every release. Beyond that, testing has largely been handled by whoever built the feature or requested it. That approach worked while the audience was small.

As Strongpoint grows, every firm we acquire moves onto the same platforms. In 2027, we are pushing for deeper adoption within each TPA, with these applications increasingly embedded in their core workflows. When a TPA runs its core work on our software, a bad release can disrupt client service. On Strongpay, it can disrupt payroll processing.

You will be our first dedicated Quality Assurance (QA) hire and will lead the function end to end. You will set the test plan, build the automation, determine what gates a release, and establish how we measure and report on quality.

There is no existing QA team to inherit, so coverage will come first from automation—including AI agents—before it comes from additional headcount.

What You Will Own

Release Testing
Own release testing across SPARK, AGL, Nexus Bridge, and Strongpay, establishing a defined quality check before code ships that is independent of the engineer who built the feature.

Automated Test Suite
Build on the unit and integration suites our engineers already maintain. Today, those tests are run manually before releases, nothing runs as an automated gate on every build, and features are checked against tickets manually.

You will build regression coverage and integrate it into the pipelines we already use, including GitHub Actions for SPARK and AGL and Azure DevOps for Strongpay.

AI Testing Agents
QA owns these. We already run agents that pick up a ticket when it moves to “Ready for QA” and test it against the acceptance criteria. Today, they serve as a first pass.

You will refine how these agents operate: what they cover, how they test, what still requires human review, and how far we can trust their results.

Load and Performance Testing
SPARK and AGL have never been tested under heavy traffic, while Strongpay carries the highest volume. You will establish load and performance testing across the platforms and identify their limits before the next onboarding finds them for us.

Rollback Planning
Strongpay snapshots its database before every deployment. SPARK and AGL do not currently have a documented process for backing out a release that causes issues in production.

You will create the rollback playbook for each platform, test it, and keep it current as our systems evolve.

Quality Metrics and Reporting
Start with escaped defects—the bugs a partner or client finds before we do. We do not currently track them.

From there, establish additional metrics including time to fix, measured from when a bug is reported until the fix is live, and hotfix frequency. Without those numbers, decisions about how quickly to ship versus how much to harden are largely a guess.

You will build the quality dashboard and own ongoing reporting.

Security Testing Before Release
Establish checks for common attack patterns as part of the release process, complementing the code scanning and annual penetration testing already in place.

A Seat in the Sprint
Work directly within the two-week sprint cycle alongside our engineers, participating from planning through demo.

What Your First Year Could Look Like

First 90 Days

Observe several release cycles and document where testing happens today across each platform and who performs it. Take ownership of the AI testing agents and begin logging escaped defects to establish a baseline.

Deliver the first automated regression suite for one high-traffic area of SPARK, AGL, or Strongpay.

By Month 6

Establish automated coverage across the core release paths of SPARK and AGL, with a coverage baseline for Strongpay beginning with payroll-critical paths.

The AI testing agents operate according to a written plan defining what they cover and what requires human review. A documented rollback plan has been used or rehearsed at least once. Baseline load-test results exist for SPARK and AGL.

A first version of the quality dashboard is live, reporting escaped defects and time to fix by platform.

By Month 12

A single dashboard reports key quality metrics across all four platforms, including escaped defects, time to fix, hotfix rate, automated coverage, and load-test results. Metrics are trended release over release and reviewed each sprint.

Strongpay and Nexus Bridge are tested to the same standard as SPARK and AGL, including load testing, and security testing is a standard step in every release.

Our Technology Stack

SPARK and AGL

• TypeScript throughout

• AGL uses Next.js and React

• SQL Server

• Hosted on Amazon Web Services (AWS) and Vercel

• Deployed with GitHub Actions

• Unit and integration testing with Jest and Vitest

Strongpay

• Mature .NET Framework application

• ASP.NET MVC

• SQL Server

• .NET MAUI mobile application for iOS and Android

• Public API

• Extensive payroll, banking, and benefits integrations

• Hosted on AWS

• Deployed with Azure DevOps

AI in Our Products

SPARK and AGL include AI-powered features such as document extraction and an AI assistant. As a result, some testing involves outputs that do not have a single “correct” answer and require evaluating quality and accuracy rather than exact matches.

Qualifications

Required

• 5+ years of experience in software QA, including at least 2 years building or owning an automated test suite from the ground up

• Experience writing test automation in code for both browser and API testing using Playwright, Cypress, or similar tools

• Hands-on experience with continuous integration and deployment (CI/CD) pipelines such as GitHub Actions or Azure DevOps, with the ability to integrate testing into an existing deployment pipeline

• Proficiency in SQL sufficient to verify the underlying data behind a test

• Load and performance testing experience using JMeter, k6, Gatling, or similar tools, including experience designing a load-test plan without an existing template

• Experience writing or owning a rollback or incident runbook and the ability to discuss how it was used in practice

• Experience working alongside engineers as a peer within a two-week sprint cycle

• Working knowledge of security testing, including common injection and authentication vulnerabilities and the OWASP Top 10

• Current experience using AI tools as part of testing work, with a clear understanding of where they add value and where human judgment is still required

Strongly Preferred

• Experience writing test code in TypeScript or JavaScript, the languages most of our automation will use

• Experience with .NET and C#, or experience building test coverage for a mature application with limited existing coverage

• Mobile application testing experience across iOS and Android

• Experience testing AI-powered features where outputs vary and testing focuses on accuracy or quality rather than exact matches

• Working knowledge of AWS, including the ability to read logs and set up a test environment

• Background in payroll, retirement plans, financial services, or another environment involving regulated or sensitive data

• Experience in a product organization that ships every one to two weeks or where multiple products share infrastructure

• Experience establishing quality reporting for leadership, including metrics such as escaped defects, time to fix, and hotfix rate