Test Architect Principal

Cigna Group•Newton, MA

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 a test framework spanning the New PBM Platform, retained and modernized ESI systems, enterprise integrations, and client re-implementation activities. It establishes shared standards for test scope, sequencing, automation, data, environments, evidence, and readiness criteria across the delivery lifecycle. The Principal partners with quality and test leads, platform and product engineering, enterprise architecture, delivery, security, infrastructure, and business readiness teams. The role provides direction on complex quality matters, resolves issues with limited precedent, and translates technical risk into recommendations for senior leaders and release-governance forums. The position defines and governs test strategy, architecture, operating models, quality roadmaps, release gates, evidence standards, and readiness practices. It owns the program’s automation and test-data strategies, including AI-assisted testing practices and controls for regulated and client data. It also aligns platform, product, integration, migration, and business testing, mentors quality professionals, and evaluates tools and techniques that improve quality, delivery cycle time, or testing cost. A bachelor's degree or equivalent related experience is required; an advanced degree is preferred. The role requires 10+ years of progressive experience in software testing, quality engineering, technology architecture, or related disciplines. Healthcare, pharmacy benefit management, regulated technology, or similarly complex transaction-processing experience is preferred. For occasional or permanent work from home, internet service must be cable broadband or fiber optic with speeds of at least 10 Mbps download and 5 Mbps upload.

Requirements

Bachelor's degree or equivalent related experience.
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 test automation at enterprise scale, 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 testing approaches for 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 testing tools and AI-enabled techniques while maintaining 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 tooling such as CloudWatch and X-Ray.
Depth in test automation tooling across interface, API, unit, and component levels, with judgment to standardize rather than accumulate frameworks.
Experience with performance and resilience tooling, contract testing and service virtualization, test-data management tooling, test-management and traceability tools, CI/CD quality and security gates, and 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.

Nice To Haves

Advanced degree.
Experience in healthcare, pharmacy benefit management, regulated technology, or similarly complex transaction-processing environments.

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 sequencing, 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.
Provide technical governance for quality decisions and independently assess whether test evidence supports readiness-gate progression.
Establish quality scorecards, risk indicators, defect measures, and executive reporting for release and migration readiness.
Define severity, triage, root-cause, risk-acceptance, and exception-management practices.
Identify systemic quality risks and drive corrective actions that improve prevention, detection, and recovery.
Advise leaders on quality tradeoffs, unresolved risks, and evidence required for go/no-go decisions.
Own the test automation strategy, including framework and tooling selection, coverage across test levels, pipeline integration, and maintainability standards.
Define test-data practices for synthetic data generation, masking and de-identification, provisioning and refresh across shared environments, and appropriate protection of PHI and client data.
Define AI-SDLC testing practices, including use of AI-generated tests, test data, and defect analysis; human review; and requirements traceability.
Establish an evidence automation approach that produces readiness evidence through the pipeline rather than manual assembly.
Set automation standards for stability, flakiness, execution time, and decisions about when manual testing is appropriate.
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 new platform capability testing, including in-factory coverage, automation, and evidence.
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 based on measurable improvements to quality, cycle time, or testing cost.
Translate technical findings into concise, decision-ready recommendations for executive and governance audiences.