Quality Assurance Manager; remote
Sioux Falls, Minnehaha County, South Dakota, 57102, USA
Listed on 2026-05-21
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IT/Tech
Data Analyst, Data Scientist, IT QA Tester / Automation -
Quality Assurance - QA/QC
Data Analyst, IT QA Tester / Automation
Overview
We are Surgence.
We are done accepting a healthcare supply chain that breaks when it's needed most. Surgence exists to keep healthcare alive in every community by transforming how the industry thinks, strategizes, and executes.
We are building more than software. We are building an industry operating hub - an orchestration layer above systems of record - where visibility becomes shared truth and insight becomes coordinated action across enterprises.
We believe fragmentation is the root cause of failure. Disconnected systems, teams, and decisions create blind spots, delay response, and drive waste - especially during disruption. Surgence exists to replace fragmentation with coordination.
As a team member at Surgence, you will be signing up for a rocket ride where reinvention is constant. We move with urgency, embrace intensity, and prioritize outcomes over activity. Charging through the storm together, we will not be satisfied until reliable care is the default - everywhere.
Surgence is a cloud-based ecosystem committed to revolutionizing the healthcare supply chain. The platform serves as a comprehensive solution for providers, distributors and suppliers, ensuring seamless coordination, efficiency and visibility across the supply chain. Surgence integrates flexible connectivity and development, advanced analytics, alerts and workflow capabilities to enable proactive decision-making, risk mitigation and collaborative forecasting to advance the healthcare supply chain.
We are 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 leading and advancing the organization s Quality Assurance program. This role provides ownership of QA strategy execution, 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 mentoring and developing a high-performing 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.
EssentialFunctions
- 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 nonfunctional 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, multitenant 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…
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