Manager, Software Development Engineering (Quality Engineering, Testing)

CVS Health•Indianapolis, IN

4d•$83,430 - $203,940•Remote

About The Position

CVS Health is seeking an SDET Manager to lead Quality Engineering and software delivery transformation across the Medicare Sales & Acquisition ecosystem. This role is critical to modernizing how we build, test, and deliver software, including embedding AI-driven capabilities into the SDLC. You will translate business workflows (Sales, Enrollment, Broker, and Telesales) into scalable, automated testing solutions that improve speed, quality, and operational efficiency. This is a hands-on technical leadership role responsible for driving test automation strategy, enabling intelligent SDLC pipelines, and building a high-performing engineering team aligned to enterprise goals. This role will also help shape the team's transition toward AWS-native test infrastructure and agent-orchestrated cycles, positioning the organization to scale automation alongside delivery volume in future years.

Requirements

7+ years of experience in Software Development Engineering, Quality Engineering, or related technical disciplines
2+ years of experience leading automation strategy and/or managing engineering teams
Strong hands-on experience with JavaScript/TypeScript, Java, or Python
Experience building and integrating APIs and distributed systems
Proven experience with test automation frameworks (e.g., Playwright, Selenium, Cypress)
Hands-on experience with AI/GenAI tools
Familiarity with Jira for workflow, defect tracking, and reporting
Ability to work East Coast business hours

Nice To Haves

Experience in Healthcare, Medicare, or Sales & Acquisition domain
Exposure to AI-driven SDLC or agent-based automation frameworks
Experience with AI-assisted testing, self-healing automation, or intelligent test optimization
Experience with AWS-native services (e.g., Bedrock, Lambda, Step Functions) in the context of test automation or CI/CD pipelines
Familiarity with Power BI for building dashboards and reporting on engineering/quality metrics
Understanding of enterprise security, governance, and compliance requirements
Strong communication skills with the ability to explain technical concepts to non-technical stakeholders

Responsibilities

Lead the design and implementation of AI-augmented Quality Engineering solutions — including agentic test generation, self-healing frameworks, and AI-assisted defect analysis — advancing the SDLC toward an AI-driven Automated Delivery Lifecycle (ADLC)
Drive the migration of automation frameworks and test data management onto AWS-native services (e.g., Bedrock for model orchestration, Lambda/Step Functions for event-driven test pipelines), enabling QE to scale elastically with delivery volume rather than being infrastructure-constrained
Advance the team's testing model from AI-assisted to agent-orchestrated QE, where autonomous agents manage larger portions of the requirement-to-release cycle (test planning, execution, triage) with human review concentrated at key decision gates
Translate business processes across Sales & Acquisition into actionable automation strategies and validation models
Design and implement automated SDLC pipelines, including requirement-to-test generation, PR impact analysis, and automated validation workflows
Establish evaluation frameworks and quality gates for AI-generated test artifacts and automation outputs, including prompt/response validation, guardrails, and responsible AI governance within the testing lifecycle
Integrate LLM-based and AI-assisted tooling (test generation, orchestration frameworks, cloud-native services) into existing test automation frameworks, CI/CD pipelines, and enterprise platforms such as GitHub, Jira, and internal data systems
Drive adoption of modern automation frameworks (UI, API, data validation) integrated into CI/CD pipelines with quality gates and regression intelligence
Manage, mentor, and grow a team of SDETs, setting technical direction and career development paths
Partner with cross-functional teams (Business, Product, Engineering, DevOps, QE) to ensure solutions are scalable, compliant, and aligned with business outcomes
Partner with business and technology teams to identify high-value opportunities for AI-driven test automation and translate them into scalable Quality Engineering solutions
Establish standards, governance models, and reusable automation assets, including prompt libraries and automation accelerators
Monitor and improve key engineering metrics such as automation coverage, defect leakage, cycle time, and delivery efficiency
Communicate automation strategy and AI-driven quality initiatives clearly to engineering teams, architects, product owners, business stakeholders, and senior leadership
Stay current with evolving automation technologies, AI/agent-based workflows, and enterprise automation best practices