We are seeking an experienced
Quality Assurance Lead to lead quality planning and testing across software, data pipelines, analytics, AI outputs, accessibility, and deliverables for a Federal data analytics and AI modernization initiative.
The team will deliver a secure, scalable platform that integrates structured and unstructured data, provides data visualization and traceable AI-assisted analytics, and gives examiners centralized tools for search, review, monitoring, and decision support. The platform will support professional judgment and will not replace authoritative agency financial or grants-management systems or execute financial transactions.
We offer competitive compensation with opportunities for bonuses, employer paid health care, training and development funds, and 401k match.
Key ResponsibilitiesQuality Strategy & Test Engineering- Develop and execute an end-to-end QA strategy spanning application functionality, data pipelines, analytics, AI-enabled capabilities, integrations, accessibility, and technical deliverables.
- Establish test plans, test cases, acceptance criteria, quality gates, and release-readiness standards across development, test, staging, and production environments.
- Lead functional, integration, system, regression, performance, security, accessibility, usability, and user-acceptance testing.
- Design and implement automated testing frameworks and integrate automated tests into CI/CD pipelines.
- Establish appropriate testing approaches for APIs, databases, data ingestion pipelines, user interfaces, and distributed services.
- Work with developers, data engineers, AI/ML engineers, platform engineers, and product owners to identify defects and quality risks early in the development lifecycle.
- Data & AI Quality
- Establish data-quality testing for accuracy, completeness, consistency, validity, timeliness, uniqueness, and data integrity across ingestion and transformation pipelines.
- Develop validation approaches to ensure data is correctly transformed and represented in downstream analytics, visualizations, reports, and applications.
- Define testing strategies for analytical models, calculations, business rules, signals, and derived metrics.
- Establish quality controls for AI/LLM-enabled functionality, including output accuracy, consistency, relevance, traceability, reliability, and appropriate handling of edge cases.
- Develop repeatable evaluation approaches for AI outputs and identify potential hallucinations, unsupported conclusions, data leakage, or other unacceptable behavior.
- Validate that AI-generated insights can be traced back to appropriate underlying data and that the platform clearly distinguishes AI-assisted analysis from authoritative source information.
- Test model and AI-service changes to ensure new versions do not introduce regressions in existing functionality or analytical results.
Required Qualifications- 7+ years of experience in software quality assurance, quality engineering, test automation, or a related discipline, with experience leading QA strategy for enterprise systems.
- Demonstrated experience developing and implementing end-to-end QA strategies for complex software, data, analytics, or AI-enabled platforms.
- Hands-on experience with automated testing and functional, integration, regression, performance, security, accessibility, and user-acceptance testing.
- Experience testing APIs, databases, web applications, data pipelines, and distributed services.
- Experience developing automated tests and integrating them into Agile/DevSecOps CI/CD pipelines.
- Experience with defect management, root-cause analysis, requirements traceability, test coverage, quality metrics, and release-readiness reporting.
- Experience developing data-quality validation and testing approaches for data ingestion, transformation, and analytics.
- Experience testing or evaluating AI/ML or LLM-enabled applications, including validation of AI-generated outputs and analytical results.
- Understanding of software development lifecycle, Agile methodologies, CI/CD, and modern application architectures.
- Familiarity with technologies such as Linux, PostgreSQL or Microsoft SQL Server, Java or .NET, Jenkins, self-hosted Azure DevOps, Kubernetes/Rancher, APIs, and open-source AI models.
- Strong analytical, problem-solving, communication, and technical documentation skills.
- Ability to work effectively with developers, data engineers, AI/ML engineers, cybersecurity professionals, product owners, and Government stakeholders.
- U.S. citizenship and ability to obtain and maintain Top Secret eligibility, as required for all contractor personnel supporting the effort.
- Active Top Secret highly preferred.
Preferred Qualifications- Experience supporting Federal financial management, budget execution, grants management, payment systems, award oversight, financial reporting, or financial stewardship is preferred.
- Experience supporting Federal financial management, grants, payment, or award-oversight platforms.
