requirements and best practices for test environments, including data management, environment parity, and CI/CD integration. Quality Signals & Evidence Standards Define what evidence is required to demonstrate quality (test results, logs, coverage, monitoring signals), and standardize how quality is measured and validated. Cross-Functional Intake & Prioritization for Quality Work Build and manage a system for capturing, prioritizing, and aligning quality-related work across Engineering, Product, and Customer-facing teams. What You’ll Do Define and evolve the end-to-end quality strategy, ensuring it scales with product complexity and company growth. Establish processes, policies, and standards that enable consistent, high-quality software delivery across teams. Partner with Engineering and Product leadership to embed quality into planning, development, and release cycles. Design a risk-based quality model, focusing efforts on the highest-impact areas rather than exhaustive coverage. Create clear feedback loops between customer-reported issues, internal quality signals, and product improvements. Build the operating system for QA, including documentation, governance, and decision-making frameworks. Manage and mentor QA engineers over time, while ensuring clarity between strategy (manager) and execution (ICs). Serve as the final escalation point for quality standards and decisions, not day-to-day test execution. What Success Looks Like Success in this role is defined by establishing a clear, scalable quality operating system that enables the organization to ship confidently and consistently—without relying on heroics or ad hoc QA effort. In the First 3–6 Months Release readiness is clearly defined and consistently enforced Every release follows a well-understood readiness policy with explicit quality gates, risk thresholds, and signoff criteria. Release decisions are grounded in evidence, not intuition. Defect triage and severity are standardized and trusted Bugs are consistently classified, prioritized, and acted on. Teams align on severity definitions, escalation paths are clear, and time-to-triage improves meaningfully. A clear regression strategy is in place and adopted Regression coverage is intentional and risk-based. Teams understand what is covered, what is not, and how regression supports release confidence without slowing velocity. Quality metrics are defined, visible, and used Core KPIs (escaped defects, defect aging, time-to-triage, reopen rate, regression suite health, flaky test rate, release confidence) are actively tracked and used to drive decisions and accountability. Test environment expectations are stabilized Environments are reliable enough to support consistent validation. Environment instability is no longer a primary source of test noise or release risk. Quality signals and evidence standards are established Teams know what “proof” is required before shipping. Test results, logs, and system signals provide clear, deterministic evidence of system behavior. A cross-functional intake and prioritization system exists for quality work Quality gaps, defects, and risk areas are visible and prioritized alongside product work. There is a clear path from signal → prioritization → resolution. In 6–12 Months The QA operating model is fully embedded across the organization Teams consistently follow defined processes for regression, triage, release readiness, and quality validation without constant oversight. Quality accountability is shared, not siloed Engineering, Product, and QA operate with a common language around risk, severity, and release confidence. QA is a force multiplier, not a bottleneck. Defect escape rates and high-severity production issues decrease There is a measurable reduction in customer-impacting issues, with faster detection and resolution when issues do occur. Regression and automation systems provide strong, low-noise signals Automation is reliable and trusted. Flaky tests are minimized, and test results meaningfully reflect system health. Release confidence increases across teams Releases happen with fewer last-minute blockers, fewer rollbacks, and less ambiguity about system readiness. Quality work is proactively identified and prioritized The system surfaces risks early—before they reach customers—through strong observability, metrics, and feedback loops. Long-Term Success A scalable QA function that runs on systems, not heroics QA engineers execute effectively within the frameworks you’ve built, with clear expectations, ownership, and leverage. A strong quality culture rooted in evidence and risk management Decisions are driven by signals, not opinions. Teams prioritize high-impact areas and focus on preventing issues rather than reacting to them. Quality becomes a competitive advantage Plenful ships quickly and reliably, with confidence in system guarantees, traceability of changes, and the ability to control risk in a complex healthcare environment. Ideal Background What We’re Looking For 10+ years of experience in QA, with 4+ years in a leadership or management role in a high-growth SaaS or enterprise environment. Strong experience defining QA strategy, quality frameworks, and scalable processes. Deep understanding of both manual and automated testing, with the ability to guide architecture (not necessarily execute). Proven experience building quality metrics, reporting systems, and governance models. Experience implementing defect triage systems, severity frameworks, and release gating processes. Strong systems thinking—able to design repeatable, scalable quality operating models. Excellent cross-functional leadership skills; able to influence Engineering, Product, and Customer teams. Clear communicator with a strong ownership mindset. Experience in healthcare, compliance-heavy environments, or structured data systems (FHIR, HL7, PDFs) is a plus. Plenful
