Director of QA

Blooming Health

38d•Remote

About The Position

Blooming Health is seeking an ambitious and highly technical Director of QA to lead the company's quality strategy, QA organization, automation framework, and release-readiness processes. This senior engineering leadership role requires operating at the intersection of quality engineering, test automation, platform reliability, AI-enabled product validation, compliance, and engineering execution. The ideal candidate will have extensive experience building scalable QA processes for complex B2B SaaS platforms and will collaborate closely with Engineering, Product, Data, AI, Security, and Customer Success to ensure the delivery of reliable, secure, and high-quality products at speed. The role is U.S.-based, with a preference for candidates in EST or CST time zones. The Director of QA will be responsible for transforming the QA function from manual/reactive testing into a mature, automation-first, data-driven quality organization that supports rapid product development, enterprise customer expectations, and regulated healthcare environments.

Requirements

Significant QA leadership experience in a B2B SaaS, health tech, fintech, enterprise software, or other complex regulated technology environment.
Proven experience building or scaling a QA function across multiple engineering teams or product pods.
Deep hands-on understanding of QA automation, test frameworks, CI/CD-integrated testing, API testing, regression testing, integration testing, performance testing, and end-to-end testing.
Strong technical credibility with engineering teams, including the ability to understand architecture, APIs, cloud systems, data flows, integrations, and complex platform dependencies.
Experience improving QA processes within the SDLC, including test planning, acceptance criteria, defect management, release readiness, root cause analysis, and quality metrics.
Experience testing complex platform products, multi-product systems, workflow automation, data-intensive applications, or enterprise integrations.
Experience working with distributed engineering and QA teams, with India-based team experience strongly preferred.
Strong cross-functional collaboration skills with Engineering, Product, Security, Customer Success, Data, AI, and Operations teams.
Ability to balance speed and quality in a fast-moving startup or growth-stage environment.
Excellent communication skills, with the ability to clearly explain quality risks, release readiness, tradeoffs, and process improvements to executives and technical teams.
Strong people leadership skills, including hiring, coaching, performance management, team development, and building a high-accountability QA culture.

Nice To Haves

Healthcare technology experience, especially within payer, provider, public health, social care, care coordination, or member engagement environments.
Experience testing healthcare integrations such as Epic, Cerner, FHIR, HL7, claims data, eligibility data, or other interoperability workflows.
Experience working in HIPAA-regulated environments.
Experience supporting SOC 2, HITRUST, NIST, or similar compliance frameworks.
Experience testing AI/ML-powered or LLM-enabled products, including agentic workflows, conversational systems, automation logic, model outputs, guardrails, and monitoring.
Experience with tools and technologies such as Cypress, Playwright, Selenium, Postman, REST APIs, GCP, AWS, MongoDB, PostgreSQL, Node.js, CI/CD pipelines, and test management platforms.
Experience introducing QA automation into organizations that previously relied heavily on manual testing.
Prior experience helping scale engineering quality through a major growth phase.

Responsibilities

Define and own Blooming Health’s overall QA strategy across web applications, backend services, APIs, integrations, data workflows, AI-enabled features, and multi-product platform capabilities.
Build and lead a scalable QA function that supports multiple engineering pods and product workstreams.
Establish quality standards, test strategies, release gates, defect management processes, and QA metrics across the engineering organization.
Partner with Engineering and Product leadership to ensure quality is built into the SDLC from planning through release and production monitoring.
Design and mature an automation-first QA framework across UI, API, integration, regression, performance, and end-to-end testing.
Increase test coverage, reduce manual regression burden, and improve release confidence.
Implement best practices for CI/CD-integrated automated testing, test data management, environment management, and quality reporting.
Evaluate and implement QA tools, frameworks, and processes that improve engineering speed, predictability, and product reliability.
Own QA readiness for major releases, roadmap commitments, customer launches, integrations, and platform changes.
Partner with engineering teams to identify risks early, prevent escaped defects, and improve delivery predictability.
Drive improvements in defect triage, root cause analysis, regression planning, and production issue prevention.
Help teams move faster without compromising quality, security, compliance, or customer trust.
Develop QA strategies for AI-enabled and agentic product experiences, including workflow automation, outreach logic, conversational flows, recommendations, and data-driven decisioning.
Partner with AI, Data, Product, and Engineering teams to validate model behavior, output quality, guardrails, edge cases, monitoring, feedback loops, and production reliability.
Ensure AI-powered features are tested for accuracy, consistency, safety, usability, explainability, and customer impact.
Support quality processes for data pipelines, integrations, analytics workflows, and member interaction data.
Partner with Engineering and DevOps to improve observability, monitoring, incident response, SLOs/SLAs, and production health.
Ensure QA contributes to broader operational maturity, including performance testing, load testing, reliability testing, and failure-mode analysis.
Create clear quality dashboards and metrics around defect trends, automation coverage, release quality, test stability, escaped defects, and customer-impacting issues.
Use data to identify systemic quality risks and drive continuous improvement across engineering teams.
Serve as the senior QA partner to Engineering, Product, Customer Success, Security, Data, AI, and Operations.
Work closely with Product to clarify acceptance criteria, edge cases, customer workflows, and release expectations.
Partner with Customer Success and Support to understand customer pain points, recurring defects, and production quality issues.
Communicate quality risks, tradeoffs, and release readiness clearly to technical and non-technical stakeholders.
Ensure QA processes support HIPAA, SOC 2, HITRUST, and other relevant compliance requirements.
Partner with Security and Engineering to validate secure-by-design practices, privacy controls, access controls, auditability, and regulated data workflows.
Support documentation, testing evidence, and process maturity required for compliance audits and enterprise customer expectations.