Quality Assurance Manager (remote)

Concordance Healthcare Solutions Careers•March Air Reserve Base, CA

1d•Remote

About The Position

Surgence is seeking a dynamic and experienced leader to lead the Customer Success team and play a pivotal role in the development of our groundbreaking supply chain technology platform. We are seeking a full-time Quality Assurance Manager in any Surgence location or fully remote. The Quality Assurance Manager is a hands-on quality leader and people manager responsible for establishing and developing the organization’s Quality Assurance function from the ground up. This role provides ownership of QA strategy, team development, testing tools, and quality processes, while embedding best practices throughout the Product Delivery Lifecycle (PDLC). This position operates as a player-coach, combining active involvement in testing execution, quality planning, and process design with leadership responsibilities for building and mentoring a scalable QA team. The Quality Assurance Manager will drive a culture of continuous improvement, accountability, and operational excellence across all quality initiatives. The scope of this role includes ensuring quality standards across B2B SaaS software solutions, data governance frameworks, and security assurance programs. The Quality Assurance Manager is accountable for delivering reliable, compliant, and high-performing releases that meet the expectations of enterprise customers and support long-term business growth.

Requirements

A bachelor’s degree in Business, Healthcare Administration, Quality Engineering, or a related field is required, or an equivalent combination of education and experience.
10+ years of experience in Quality Assurance and/or Quality Engineering, including at least 5 years in a people-leadership role, with demonstrated success building, scaling, and maturing QA functions, teams, and operating practices.
Strong background in B2B SaaS quality assurance, including experience with multi-tenant platforms, configurable customer environments, system integrations, and enterprise-grade release readiness spanning functional, regression, and performance validation.
Experience with data quality and data governance is required, including validation of data integrity, lineage, analytics outputs, and reporting controls across systems.
Experience supporting security and compliance assurance activities, including validation of access controls, permissions, and testing requirements in regulated environments.
Working knowledge of modern automation and AI-enabled quality practices is expected, including the use of AI tools to accelerate test design, automation, analysis, and documentation, with strong judgment around verification, traceability, and protection of sensitive data.
Technical proficiency in Foundry-relevant languages and paradigms such as Python, SQL, and ontology-driven design is required, along with the ability to evaluate AI-generated code and test artifacts for correctness, maintainability, and risk.
Comfortable operating in a player-coach capacity, balancing hands-on execution with leadership, mentoring, and team development.
Strong analytical, problem-solving, and decision-making skills are essential, along with a proactive and pragmatic approach to quality and risk management in fast-moving, ambiguous environments.
Excellent communication and collaboration skills are required, including the ability to translate business and stakeholder needs into effective quality strategies and technical solutions.
Employment is contingent upon the ability to successfully pass a post-offer pre-employment background check and drug screening.

Nice To Haves

Experience in healthcare and/or supply chain domains is preferred, as is comfort working in regulated environments or closely with operational and logistics teams supporting non-technical end users.

Responsibilities

Lead and execute the end-to-end Quality Assurance strategy aligned to the Product Delivery Lifecycle (PDLC), embedding quality from discovery through release and operations, including AI-assisted delivery workflows.
Define and enforce release-readiness standards covering functional and non-functional quality, data accuracy, security, compliance, and operational stability.
Establish and execute risk-based test strategies (manual and automated), including exploratory testing, regression coverage, and validation of complex, multi-tenant B2B SaaS platforms.
Own data quality assurance practices, including validation of data integrity, lineage, analytics outputs, and governed metrics across ingestion, transformation, and application layers.
Embed security and compliance validation into QA practices, including access controls, secure data handling, and environment safety checks.
Select, implement, and optimize QA tools, frameworks, and CI/CD-integrated testing approaches that balance speed with release confidence.
Define validation and monitoring strategies for AI/ML-enabled features, including accuracy, bias, edge-case testing, and regression or drift detection where applicable.
Provide clear visibility into quality posture through defect trends, release readiness assessments, and documented risk, escalating issues that could impact customers or commitments.
Provide visible, hands-on leadership for complex, high-risk, or first-of-kind initiatives, modeling sound judgment, technical credibility, and calm decision-making in regulated, highly integrated, or ambiguous environments.
Operate as a trusted partner to Product, Engineering, Delivery, Data, and Security leadership, fostering early alignment, surfacing cross-functional risks, and resolving issues through collaboration rather than downstream escalation.
Build and sustain a high-performing Quality Assurance organization through thoughtful hiring, intentional onboarding, coaching, mentoring, and performance management.
Define and evolve QA roles, responsibilities, career paths, and growth expectations that scale with platform complexity and organizational maturity.
Champion a culture of shared accountability for quality, influencing teams to own outcomes while keeping QA lightweight, pragmatic, and enablement-focused.
Establish organizational guardrails for responsible AI usage across QA and Engineering, reinforcing expectations around data privacy, auditability, transparency of AI-generated artifacts, and appropriate human oversight.
Serve as a risk and readiness advisor to senior leadership, clearly framing tradeoffs, recommending release decisions, and grounding discussions in evidence rather than process.
Escalate material risks with clarity and proportionality, ensuring potential impacts to customer trust, regulatory obligations, or business commitments are understood early.
Influence priorities, timelines, and commitments through credible risk assessment, clear communication, and principled decision-making - balancing speed with long-term quality and trust.