Quality Engineering Lead - Fraud Technology

The Opportunity 

We are seeking an experienced Quality Engineering Lead to lead testing and quality engineering activities across Definity's Fraud Technology portfolio. This role combines strong quality engineering expertise with hands-on delivery experience, ensuring high-quality, scalable, and reliable fraud solutions that support fraud detection, prevention, investigation, and fraud intelligence capabilities.

The successful candidate will act as a senior individual contributor responsible for test planning, test strategy development, test case design, automation, execution, defect management, and quality reporting across complex fraud technology initiatives. Working closely with Fraud Technology, Product Owners, Data Engineering, Claims, Underwriting, and vendor teams, the individual will help ensure end-to-end fraud solutions are delivered with a high degree of quality, reliability, and business confidence

What to Expect

Quality Engineering Accountabilities

• Define and oversee test strategies, test plans, test schedules, resource plans, and quality gates.
• Lead QE effort estimation, planning assumptions, resource forecasting, and delivery planning.
• Establish risk-based testing approaches aligned with solution complexity and business criticality.
• Ensure complete test coverage across functional, integration, data, end-to-end, regression, exploratory, and non-functional testing.
• Lead quality reviews of testing deliverables and ensure adherence to enterprise QE standards and processes.
• Participate in release governance, gate reviews, and readiness assessments.
• Provide recommendations regarding production readiness and deployment quality risks.
• Support the execution of functional, system, integration, end-to-end, regression, and exploratory testing activities.
• Design and implement automated testing solutions where appropriate.
• Participate in requirements reviews and ensure business and technical requirements are testable and fully validated.
• Contribute to ongoing QE continuous improvement through the use of innovation focused on quality, automation, delivery efficiency, and risk reduction.
  

Fraud Technology Testing

• Oversee testing and validation activities for Fraud Detection, Fraud Prevention, Fraud Intelligence, and Fraud Investigation capabilities.
• Ensure quality validation of fraud intelligence and risk-scoring capabilities, establish testing approaches for advanced fraud analytics and fraud data models.
• Validate fraud rules, scoring models, alerts, integrations, workflows, case management capabilities, and fraud decisioning processes.
• Validate solutions leveraging graph analytics, entity resolution, relationship mapping, network intelligence, and structured/unstructured data used to identify suspicious relationships, connected entities, and fraud networks.
• Support testing of AI, machine learning, predictive analytics, anomaly detection, and advanced fraud detection capabilities while partnering with Fraud, Claims, Underwriting, Data Engineering, and Technology teams to ensure business requirements are effectively validated.
  

What You Bring

• Undergraduate or Postgraduate degree in Computer Science, Engineering, Information Technology, or a related discipline.
• 10+ years of experience in Quality Engineering, Software Testing, or Quality Assurance.
• 3+ years leading QE teams, testing programs, or complex enterprise quality initiatives.
• Proven experience within the Property & Casualty Insurance industry, with exposure to Claims, Underwriting, Fraud, or Risk Management processes preferred
• Proven experience supporting fraud technology, fraud analytics, fraud detection, fraud prevention, or fraud investigation solutions, with exposure to one of more of the following areas preferred: 
  • Experience with graph databases, graph network analytics, entity resolution, relationship mapping, or network intelligence platforms.
  • Experience validating structured and unstructured data used within fraud analytics and investigative solutions.
  • Experience testing AI/ML-enabled fraud detection, predictive analytics, anomaly detection, or fraud intelligence platforms.
  • Knowledge of fraud investigation workflows, fraud scoring, fraud decisioning, and fraud risk management practices.
• Experience developing test strategies, test plans, test scenarios, test cases, and automation solutions for complex enterprise applications.
• Strong understanding of defect management, root cause analysis, and quality engineering best practices.
• Experience with Agile software delivery methodologies.
• Working knowledge of Jira and Zephyr.
• Strong analytical, problem-solving, stakeholder management, and communication skills.
• Proven ability to adapt quickly, remove roadblocks, and deliver results.
• Experience working effectively in onsite/offshore delivery models.
• Ability to work within a hybrid work environment.

Salary Range: $108,700-$158,700