Test Architect Principal

Cigna Healthcare•Newton, MA

5d•$151,600 - $252,600

About The Position

The PBM Test Architect Principal serves as the senior technical authority for end-to-end testing strategy, quality architecture, and release-readiness evidence across the New PBM Platform program. The role defines the test framework spanning the new platform, retained and modernized ESI systems, enterprise integrations, and client re-implementation activities. It establishes common standards for test scope, sequencing, automation, data, environments, evidence, and readiness criteria across the delivery lifecycle. The Principal partners with quality and test leads and with platform and product engineering, enterprise architecture, delivery, security, infrastructure, and business readiness teams. The role provides direction on complex quality and testing matters, resolves issues with limited precedent, and translates technical risk into recommendations for senior leaders and release-governance forums. The position owns test strategy and architecture, quality governance and readiness, automation and test data strategy, and testing practices within an AI-assisted software development lifecycle. It sets standards for testing across both new-platform development and the existing estate, coordinates cross-program testing, mentors quality professionals, and evaluates tools and techniques that can measurably improve quality, cycle time, or cost. The anticipated annual salary is $151,600–$252,600, depending on relevant factors including experience and geographic location. The role is anticipated to be eligible for an annual bonus and long-term incentive plan. If working from home occasionally or permanently, an internet connection through a cable broadband or fiber-optic provider with speeds of at least 10 Mbps download and 5 Mbps upload is required.

Requirements

Bachelor’s degree or equivalent related experience required.
10+ years of progressive experience in software testing, quality engineering, technology architecture, or related disciplines.
Demonstrated experience defining enterprise-scale test strategies and governing quality across multiple engineering and delivery organizations.
Proven success influencing senior technical leaders and executives without relying on direct authority.
Experience supporting large-scale platform transformation, systems integration, or enterprise migration programs.
Hands-on experience building and governing enterprise-scale test automation, including framework design, CI/CD integration, and test data management under regulated data constraints.
Experience testing within an AI-assisted software development lifecycle, including AI-generated tests and test data, and the validation and traceability controls required to rely on them.
Strong written, verbal, analytical, and presentation skills.
Deep expertise in enterprise test architecture, quality engineering, automation strategy, and software-delivery controls.
Experience designing test approaches for complex distributed platforms, APIs, event-driven integrations, data pipelines, and legacy-modern ecosystems.
Strong knowledge of performance, scalability, resilience, disaster recovery, security, accessibility, and other non-functional testing disciplines.
Experience with test data management, environment strategy, service virtualization, observability, CI/CD integration, and evidence automation.
Ability to assess emerging testing tools and AI-enabled techniques while maintaining appropriate security, privacy, compliance, and validation controls.
Hands-on experience testing cloud-native workloads on AWS, including containerized and serverless services, managed data and messaging services, and observability tools such as CloudWatch and X-Ray.
Depth in test automation tooling across interface, API, unit, and component layers, with judgment to standardize rather than accumulate frameworks.
Experience with performance and resilience tooling and with contract testing and service virtualization.
Experience with test data management tooling, including masking, subsetting, and synthetic generation for regulated data.
Experience with test management and traceability tooling and with embedding quality and security gates in CI/CD platforms alongside scanning tools.
Practical experience with AI-assisted engineering tools and AI-enabled testing capabilities, including generated test cases and data, self-healing automation, and visual validation.
Ability to translate highly technical findings into concise, decision-ready recommendations for executive and governance audiences.

Nice To Haves

Advanced degree preferred.
Experience in healthcare, pharmacy benefit management, regulated technology, or similarly complex transaction-processing environments preferred.
Experience with illustrative tools named in the posting, including Playwright, Selenium, Cypress, REST Assured, Karate, Postman, JUnit, TestNG, pytest, JMeter, Gatling, k6, LoadRunner, Pact, WireMock, Delphix, Informatica TDM, GenRocket, Tonic, Jira with Xray or Zephyr, qTest, TestRail, Jenkins, GitHub Actions, GitLab CI, Harness, SonarQube, Snyk, Checkmarx, Claude Code, GitHub Copilot, Amazon Q Developer, or Cursor; the posting says these products illustrate expected depth and are not individually required.

Responsibilities

Define and maintain the program’s end-to-end test strategy, architecture, operating model, and multi-year quality roadmap.
Establish standards for unit, contract, integration, end-to-end, regression, performance, resilience, security, data-conversion, migration, and operational-readiness testing.
Define release ladders, entry and exit criteria, evidence standards, sign-off expectations, and traceability requirements across delivery teams.
Create reusable testing patterns and reference architectures that support platform scale, frequent releases, and successive client waves.
Ensure test architecture aligns with enterprise architecture, security, privacy, compliance, environment, and data-handling requirements.
Govern quality decisions and independently assess whether test evidence supports progression through readiness gates.
Establish quality scorecards, risk indicators, defect measures, and executive reporting on release and migration readiness.
Define severity, triage, root-cause, risk-acceptance, and exception-management practices across the program.
Identify systemic quality risks and drive corrective actions to improve prevention, detection, and recovery.
Advise leaders on quality tradeoffs, unresolved risks, and evidence needed for go/no-go decisions.
Own the test automation strategy, including framework and tooling selection, coverage balance, pipeline integration, and maintainability standards.
Define test data strategy, including synthetic data generation, masking and de-identification, provisioning and refresh models, and controls for PHI and client data.
Define testing practices within the AI-assisted software development lifecycle, including human review of AI-generated assets and requirements traceability.
Establish evidence automation so readiness evidence is produced through pipelines rather than assembled manually.
Set automation standards, including stability and flakiness thresholds, execution time, and criteria for choosing manual testing.
Partner with the Build Quality Lead to ensure in-factory testing follows program strategy and quality standards.
Partner with the Integration and End-to-End Test Lead to ensure cross-estate and client-wave testing produces complete, reliable evidence.
Set direction for the PBM Test Lead on testing new platform capability and ensuring coverage, automation, and evidence meet program standards.
Set direction for the PBS Test Lead on existing-estate regression, cross-estate integration, and client-wave testing.
Convene PBM and PBS Test Leads to resolve shared-environment, cross-estate test-data, defect-ownership, and test-window conflicts.
Align platform, product, integration, migration, and business testing into a coherent strategy and sequence.
Mentor architects, engineers, and quality professionals and elevate test engineering capability across the program.
Evaluate emerging testing tools and techniques and make adoption decisions where they measurably improve quality, cycle time, or testing cost.
Translate technical findings into concise, decision-ready recommendations for executive and governance audiences.