As the Lead QA Engineer, you will own quality across a broad technology landscape spanning legacy application, modern AWS platforms, APIs, data engineering, automation, and emerging AI solutions.
This is a hands-on automation engineering role, not traditional manual QA. You will build automated testing frameworks, integrate quality gates into delivery pipelines, lead QA resources, identify risk, and provide clear evidence that solutions are ready for production.
Strong Python, AWS, test automation, API, and data testing skills are essential. The most important qualities are strong engineering fundamentals, attention to detail, and the ability to make objective, evidence-based quality decisions.
Responsibilities
Own the test strategy, quality standards, coverage, and release criteria for Actuarial and Finance technology.
Build and maintain Python/pytest automated testing frameworks for applications, APIs, data, and backend services.
Integrate automated tests into CI/CD pipelines(Harness.io), automated security scan validation and establish quality gates that identify and block unacceptable release risk.
Test AWS applications and services, including serverless and event-driven solutions.
Validate data pipelines, transformations, reconciliation, completeness, and reporting data using SQL and automated testing.
Test end-to-end business processes across applications, data, integrations, and batch systems.
Build regression and evaluation testing for traditional & AI/LLM solutions, including prompt, model, tool-use, and output changes.
Validate that AI-driven actions have appropriate human approvals, security controls, and audit trails.
Partner with Security to test authentication, authorization, IAM, data protection, and AI-specific security risks.
Maintain clear test evidence, traceability, defect reporting, and release-quality metrics.
Lead QA engineers and contractors, review test designs, and continuously improve testing practices.
Clearly communicate quality risks and production readiness to technology and business stakeholders.
Qualifications
Bachelor's degree in computer science, Engineering, Information Systems, Mathematics, or related field.
7+ years of software quality engineering experience.
Strong hands-on experience with Python and pytest, including building reusable automated test frameworks.
Strong experience with API testing, including REST APIs, contracts, negative scenarios, and integration testing.
Experience testing applications on AWS, including services such as Lambda, Step Functions, EventBridge, SQS, and SNS.
Experience integrating automated tests into CI/CD pipelines using tools such as Harness, GitHub, or Bitbucket.
Strong SQL and data testing experience using platforms such as Athena, Snowflake, Redshift, SQL Server, or equivalent.
Experience validating data pipelines, transformations, reconciliation, completeness, and data integrity.
Experience with test data management, including creating, masking, refreshing, and maintaining repeatable test data.
Experience defining test strategy, risk-based coverage, acceptance criteria, and release gates.
Experience with Jira and qTest, or similar defect and test management tools.
Understanding of authentication, authorization, IAM, audit logging, and security testing.
Experience producing test evidence for SOX, ITGC, audit, or similar regulated controls.
Experience leading QA engineers, testers, or contractors.
Strong written and verbal communication skills, with the ability to clearly explain quality risks to technical and business stakeholders.
Preferred qualifications
Experience testing AI, machine learning, or LLM-based applications.
Experience building LLM evaluation and regression testing for prompts, models, agents, and AI-generated outputs.
Experience with Amazon Bedrock or similar managed AI platforms for accelerated testing.
Experience with AI observability/evaluation tools such as LangSmith, Langfuse, Promptfoo, or OpenTelemetry.
Familiarity with prompt…