QA Chapter Lead

About impact.com

impact.com is the world's leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products—Performance (affiliate), Creator (influencer), and Advocate (customer referral)—unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands, including Walmart, Uber, Shopify, Lenovo, L'Oréal, and Fanatics, rely on impact.com to power more than 225,000 partnerships that deliver measurable business results.

Your Role atimpact.com:

The QA Chapter Lead sets the quality bar across our IO and AI groups and makes engineering teams the owners of it. This is a leadership role built on influence rather than a separate reporting line: you raise the standard of quality by shaping how teams design, build, and ship, by coaching engineers to own testing end to end, and by building the tooling and evaluation systems that make quality the path of least resistance. You will treat quality as an engineering discipline embedded in every team, not as a downstream gate a dedicated QA person is responsible for. The role deliberately spans two very different delivery contexts. The IO group builds deterministic platform software: integrations, ETL pipelines, APIs, and the performance and security concerns that come with them. The AI group builds non-deterministic, agentic products where correctness, safety, cost, and latency are all quality dimensions and where traditional test scripts do not apply. You will build one quality discipline that works across both.

What You'll Do:

As a QA Lead for the platform group, you will lead a critical function focused on both the technical infrastructure and the human element of Quality Assurance.

Your responsibilities will include:

1. Set the quality bar and the operating model

• Define what "good" looks like across both groups, and make engineers the owners of that bar rather than the QA function.
• Shift quality upstream, from a downstream gate into how teams scope, design, and build, so that testing is part of engineering rather than a phase after it.
• Establish quality signals teams actually use to make decisions (escaped defects, mean time to detect, confidence to ship, change failure rate) over vanity metrics like raw test-case counts.
• Drive continuous improvement of how the groups engineer for quality, and retire practices that no longer earn their keep.

2. Lead the chapter, not a QA queue

• Grow a cross-group chapter and community of practice for quality engineering spanning IO and AI, aligning standards without centralising the work.
• Coach engineers to own testing for their own work, increasingly with agents doing the heavy lifting. Lead through standards, reviews, pairing, and reusable patterns rather than by running a separate QA backlog that work is handed off to.
• Raise the floor for everyone: onboarding, patterns, reusable harnesses, and the internal enablement that lets any engineer test well by default.
• Provide technical mentorship and feedback that grows quality capability inside the engineering teams.

3. Quality engineering in the agentic age

• Treat agents as first-class contributors to testing: generating tests, test data, and exploratory coverage, triaging failures, and widening coverage, with humans designing the harnesses, guardrails, and reviews that keep them honest.
• Build the evaluation systems for AI products: evals, regression suites for non-deterministic output, and safety, hallucination, and guardrail testing, alongside strong classic automation for the platform.
• Own the tooling, harnesses, and CI/CD guardrails that make the quality bar automatic rather than a matter of individual diligence.
• Stay ahead of where AI-assisted testing and agentic engineering are going, and bring the useful parts back into both groups.

4. Test strategy across two contexts

• For the IO platform: functional, regression, integration, performance, and security testing, with strong automation and quality gates in CI/CD.
• For AI products: eval-driven testing for non-deterministic and agentic behaviour, including prompt and behaviour regression, safety, and cost and latency as first-class quality signals.
• Ensure risk-based coverage in both, so effort follows blast radius rather than spreading evenly.
• Make release readiness a team decision backed by shared signals, with your discipline setting the standard for what "ready" means.

5. Influence and collaboration

• Partner with Engineering Leads and Product Managers to move quality left and to design in testability and clear acceptance criteria from the start.
• Communicate quality posture, risks, and the signals behind them to stakeholders, and inform go/no-go decisions with data while ownership stays with the teams.
• Partner with QA platform and enable the ability to evaluate and adopt next-generation tools and techniques for the discipline.

Essential requirements:

• 5+ years of experience in Quality Assurance and/or Software Development

• Engineering in Test (SDET) background, with a strong background in test automation and platform development.

• Champion of Quality: Proven track record of owning and upholding the absolute standard of software quality for an entire group or department.

• Leadership through influence: a track record of raising quality across teams you did not directly manage, by setting standards and coaching rather than by owning a central QA queue.

• A multiplier, not a gatekeeper: proven ability to make other engineers own and raise quality, so the bar holds without you personally testing everything or acting as the release gate.

• Agentic AI expertise: Practical familiarity with putting AI at the forefront to improve engineering workflows, including experience with MCPs, Agents and Agent Skills, and utilizing/building testing harnesses.

• Testing AI systems: practical experience evaluating non-deterministic or agentic products, for example building eval harnesses and regression suites for LLM or agent behaviour, and testing for safety and guardrail failures.

• Infrastructure foundations: strong understanding of cloud ecosystems, containerisation (Docker), ETL pipelines, and API endpoints.

• Deep expertise in testing methodologies, tools, and frameworks (e.g. Playwright, Selenium, TestRail, API testing, CI/CD integration).

• Excellent communication and influence, with the ability to align engineers, product, and leadership around a quality bar they choose to own.

• Proactive, first-principles mindset, always looking for ways to improve quality, flow, and developer experience rather than to add process. Nice-to-have requirements:

• Experience leading test framework or tooling migrations.

• Familiarity with OWASP guidelines and security testing tools.

• Interest in SRE principles and treating reliability as a quality concern.

• Experience embedding quality practices into teams that had none, or moving a team off a dedicated-QA model. Benefits and Perks:

At impact.com, we believe that when you're happy and fulfilled, you do your best work. That's why we've built a benefits package that supports your well-being, growth, and work-life balance.

• Flexible Working: Our Responsible PTO policy means you can take the time off you n