QA Engineer

Key responsibilities

• Define the performance test strategy for the 2x, 3x and 5x targets: workload model, test types (load, stress, soak, spike, scale-out) and pass criteria.
• Build realistic workload models from current claims patterns: nightly batch, EDI intake (837 claims, 277CA acknowledgements), real-time API calls and event traffic.
• Design and run load tests on a production-sized Azure environment, and prove horizontal scale-out to 5x by adding resources rather than changing code.
• Find and fix bottlenecks with the engineering pods across Go services, PostgreSQL, Kafka and the infrastructure.
• Set performance budgets per service (throughput, latency, batch duration, error rate) and enforce them in CI/CD.
• Publish load-test results as a go / no-go input to each production cutover.

QA architecture across the pods

• Own test standards, the automation framework and QA oversight across all six delivery pods.
• Lead the Validation pod (2 Automation QA engineers).
• Build automated tests from Gherkin acceptance scenarios, including API, integration, end-to-end and regression suites.
• Design the parity tests that compare Prism's claim decisions with the legacy Que system, aiming for 99.5% decision match.
• Support parallel runs, reconciliation and UAT defect triage around each cutover.

AI-driven delivery (AI-DLC)

• Use AI coding agents (GitHub Copilot and similar) to generate test cases, load scripts and synthetic test data from specifications.
• Set review standards and quality gates for AI-generated tests and code, so AI output is checked before it is trusted.
• Contribute to AI-DLC retrospectives: tune prompts, agents and the sizing model, and keep AI tool spend within budget.

Data and compliance

• Work only with synthetic, non-production data offshore. No protected health information (PHI) reaches offshore staff or AI tools.
• Define synthetic data volumes and profiles that are realistic enough for 5x load tests and repeatable for every release.

Experience

• 12+ years in QA, with 5+ years as a performance or QA architect on high-volume transaction systems (claims, payments, banking or similar).
• Hands-on load testing with k6 (preferred), JMeter, Gatling or Locust, including scripting, distributed load generation and results analysis.
• A track record of capacity and scale-out testing: has designed and run tests that proved a system at several times its current volume, and can show the results.
• Workload modelling from production patterns, covering both batch windows and real-time traffic.
• Performance analysis across the stack: APM and observability (Grafana, Prometheus, Datadog, Dynatrace or Azure Monitor), PostgreSQL query plans and tuning, Kafka throughput and consumer lag.
• Performance testing of containerised, cloud-native services on Azure or another major cloud, including autoscaling behavior.
• Test automation architecture: framework design, API testing, BDD with Gherkin, and test gates in CI/CD pipelines (GitHub Actions or Azure DevOps).
• Daily use of AI coding assistants (GitHub Copilot or similar) for test design, script generation or test data, with clear examples of reviewing and correcting AI output.
• Experience leading QA engineers and setting standards across several agile teams.
• Clear written and spoken English for daily work with US-based client stakeholders.

Good to have

• US healthcare claims domain: claim lifecycle, payer submission, EDI X12 837 and 277CA, HIPAA.
• Legacy-to-cloud modernization, especially strangler-pattern migrations with parallel runs and data reconciliation.
• Parity or shadow testing that compares a new system's outputs with a legacy system's.
• Synthetic test data generation at volume.
• Performance testing of Go services.
• Chaos or resilience testing (failover, recovery, Kafka partition rebalancing).
• Security testing in CI/CD, such as SAST with SonarCloud.
• Certifications such as ISTQB Advanced (Test Analyst or Technical Test Analyst) or a cloud certification such as Azure Administrator or Developer.