• 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 the release ladder, entry and exit criteria, evidence standards, sign-off expectations, and traceability requirements used across every delivery team in the program.
• 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.

Quality Governance & Readiness

• Provide technical governance for quality decisions and independently assess whether test evidence supports progression through readiness gates.
• Establish quality scorecards, risk indicators, defect measures, and executive reporting that present a clear view of 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 that improve prevention, detection, and recovery.
• Advise leaders on quality tradeoffs, unresolved risks, and the evidence required for go/no-go decisions.

Automation, Test Data & AI-Assisted Quality

• Own the test automation strategy for the program, covering framework and tooling selection, the balance of coverage from unit through end-to-end, pipeline integration, and the standards that keep automation maintainable as the platform changes.
• Define the test data strategy, including synthetic data generation, masking and de-identification of production-derived data, provisioning and refresh models across shared environments, and the controls that keep PHI and client data handled appropriately.
• Define how testing is performed within an AI-assisted software development lifecycle (AI-SDLC), including the use of AI-generated tests, test data, and defect analysis, the human review required before generated assets are trusted, and how traceability back to requirements is preserved.
• Establish the evidence automation approach so readiness evidence is produced as a byproduct of the pipeline rather than assembled by hand.
• Set the standard for what good automation looks like, including stability and flakiness thresholds, execution time, and the decision rules for when a manual test remains the right answer.

Cross-Program Technical Leadership

• Partner with the Build Quality Lead to ensure in-factory testing executes against the program strategy and quality bar.
• 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 of new platform capability, confirming that in-factory coverage, automation, and evidence meet the program standard before capability leaves the build teams.
• Set direction for the PBS Test Lead on testing across the existing estate, confirming that regression of current systems, integration across both estates, and client-wave testing produce evidence that holds at the readiness gate.
• Convene the PBM and PBS Test Leads on a standing cadence to resolve seam issues, including shared environments, test data spanning both estates, defect ownership when a failure crosses the boundary, and whose schedule wins when test windows collide.
• Align platform, product, integration, migration, and business testing into one 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 the adoption call where they measurably improve quality, cycle time, or cost of testing.

Critical Leadership Competencies

• Drives Vision and Purpose: Connects the role’s work to the broader PBM transformation objectives and communicates a clear path forward.
• Delivers Results: Creates accountability, removes barriers, and drives measurable outcomes under demanding timelines.
• Manages Complexity: Synthesizes complex and sometimes conflicting information to establish practical solutions.
• Decision Quality: Makes sound, timely decisions using technical, delivery, risk, and business considerations.
• Collaborates: Builds effective partnerships across engineering, architecture, product, delivery, and business teams.
• Communicates Effectively: Provides clear, concise, and audience-appropriate communications, recommendations, and escalations.
• Manages Ambiguity: Establishes direction and momentum where requirements, solutions, or precedents are still emerging.
• Instills Trust: Builds credibility through transparency, expertise, follow-through, and sound judgment.

Critical Technology Competencies

• 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 the observability tooling used to diagnose failures such as CloudWatch and X-Ray.
• Depth in test automation tooling across layers, such as Playwright, Selenium, or Cypress at the interface level, REST Assured, Karate, or Postman at the API level, and JUnit, TestNG, or pytest at the unit and component level, with the judgment to standardize rather than accumulate frameworks.
• Experience with performance and resilience tooling such as JMeter, Gatling, k6, or LoadRunner, and with contract testing and service virtualization such as Pact, WireMock, or commercial equivalents.
• Experience with test data management tooling such as Delphix, Informatica TDM, GenRocket, or Tonic, including masking, subsetting, and synthetic generation for regulated data.
• Experience with test management and traceability tooling such as Jira with Xray or Zephyr, qTest, or TestRail, and with embedding quality and security gates in CI/CD platforms such as Jenkins, GitHub Actions, GitLab CI, or Harness, alongside scanning tools such as SonarQube, Snyk, or Checkmarx.
• Practical experience with AI-assisted engineering tooling such as Claude Code, GitHub Copilot, Amazon Q Developer, or Cursor, and with AI-enabled testing capabilities including generated test cases and data, self-healing automation, and visual validation.
• Products named above are illustrative of the depth expected in each category rather than individually required.
• Ability to translate highly technical findings into concise, decision-ready recommendations for executive and governance audiences.

• Bachelor's degree or equivalent related experience required; advanced degree preferred.
• 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 (AI-SDLC), 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.
• Experience in healthcare, pharmacy benefit management, regulated technology, or similarly complex transaction-processing environments preferred.

If you will be working at home occasionally or permanently, the internet connection must be obtained through a cable broadband or fiber optic internet service provider with speeds of at least 10Mbps download/5Mbps upload.

For this position, we anticipate offering an annual salary of 151,600 - 252,600 USD / yearly, depending on relevant factors, including experience and geographic location.

This role is also anticipated to be eligible to participate in an annual bonus and long term incentive plan.

At The Cigna Group, you’ll enjoy a comprehensive range of benefits, with a focus on supporting your whole health. Starting on day one of your employment, you’ll be offered several health-related benefits including medical, vision, dental, and well-being and behavioral health programs. We also offer 401(k), company paid life insurance, tuition reimbursement, a minimum of 18 days of paid time off per year, paid holidays, and leaves of absence. For more details on our employee benefits programs, click here .

About The Cigna Group

Doing something meaningful starts with a simple decision, a commitment to changing lives. At The Cigna Group, we’re dedicated to improving the health and vitality of those we serve. Through our divisions Cigna Healthcare and Evernorth Health Services, we are committed to enhancing the lives of our clients, customers and patients. Join us in driving growth and improving lives.

The Cigna Group has a tobacco-free policy and reserves the right not to hire tobacco/nicotine users in states where that is legally permissible. Candidates in such states who use tobacco/nicotine will not be considered for employment unless they enter a qualifying smoking cessation program prior to the start of their employment. These states include: Alabama, Alaska, Arizona, Arkansas, Delaware, Florida, Georgia, Hawaii, Idaho, Iowa, Kansas, Maryland, Massachusetts, Michigan, Nebraska, Ohio, Pennsylvania, Texas, Utah, Vermont, and Washington State.

Qualified applicants with criminal histories will be considered for employment in a manner consistent with all federal, state and local ordinances.