Performance Engineering - Sr. QA Manager

McKesson•Atlanta, GA

11d•$121,500 - $202,500•Hybrid

About The Position

McKesson is seeking a senior, hands-on Performance Engineering Sr. QA Manager (M3) to lead a Performance Engineering Center of Excellence and own QA delivery for a product portfolio supporting high-transaction healthcare platforms. The role focuses on improving the reliability, scalability, and performance of EMR/EHR systems while mentoring QA teams and influencing engineering decisions with data. The manager will shape performance strategy, build realistic workload models, diagnose complex bottlenecks, leverage observability, guide tooling architecture, and strengthen QA processes, quality metrics, and release readiness across systems built on microservices, APIs, cloud-native platforms, and modern frontend architectures. The role collaborates regularly with distributed engineering, QA, and DevOps teams across US and India time zones. It is hybrid: the selected candidate is expected to work onsite at the Las Colinas office at least two days per week, with remaining days worked remotely. Specific in-office days may be designated according to team needs and business priorities. Applicants must be authorized to work in the United States on a full-time basis without current or future employer immigration support or sponsorship. McKesson offers a competitive compensation package; the stated base pay range is $121,500–$202,500. Annual bonus or long-term incentive opportunities may also be offered.

Requirements

12+ years of progressive Quality Engineering and/or Performance Engineering experience.
4+ years in formal people management or engineering leadership roles managing QA teams of five or more engineers.
Ability to personally design, script, execute, analyze, and communicate performance tests and findings.
Hands-on experience with JMeter, LoadRunner, k6, Gatling, NeoLoad, or equivalent, including advanced scripting, protocol-level testing, workload modeling, distributed load generation, correlation, and parameterization.
Ability to program and script in Java, Groovy, Python, or equivalent languages to build test scripts, utilities, custom extensions, and automation support.
Strong working knowledge of distributed architectures, including microservices, APIs, API gateways, event-driven systems, containers, Java Spring Boot services, and React-based frontend or micro-frontend applications.
Experience integrating quality and performance validation into CI/CD pipelines using Jenkins, GitLab CI, Azure DevOps, or equivalent, including automated execution, pipeline reporting, performance gates, and release thresholds.
Strong understanding of system performance tuning across JVM internals, garbage collection, thread analysis, connection pools, application server configuration, OS and infrastructure utilization, and autoscaling behavior.
Practical experience with Dynatrace or comparable APM tools, distributed tracing, dashboards, alerting, proactive monitoring, and performance analysis across Oracle, PostgreSQL, MongoDB, and Redis, including query tuning and indexing.
Strong troubleshooting capability across application code, JVM, database, network, infrastructure, and frontend layers, with ability to translate findings into actionable engineering recommendations.
Ability to define and guide automation strategy using Selenium, Playwright, RestAssured, API automation, regression coverage targets, framework governance, coverage thresholds, and automation ratio tracking.
Hands-on experience using AI-assisted QA, test automation, code generation, analysis, or productivity tools to improve coverage, delivery speed, engineering effectiveness, and quality outcomes.
Proven ability to lead QA strategy, test planning, QA process maturity, automation maturity, release sign-off, quality metrics, NFR governance, process improvement, and stakeholder communication across product portfolios.
Must be currently authorized to work in the United States full-time without the need for employer support or sponsorship now or in the future.

Nice To Haves

Healthcare technology, EMR, EHR, clinical workflow, patient-facing platform, or other mission-critical healthcare product experience.
Experience building or scaling a Performance Engineering CoE, community of practice, shared tooling library, governance model, or internal enablement program.
Experience with Kubernetes, container resource tuning, autoscaling, and cloud platforms such as AWS, Azure, or GCP.
Knowledge of healthcare interoperability standards such as HL7 and FHIR, and awareness of healthcare data sensitivity, HIPAA, audit readiness, and collaboration with compliance, security, and legal partners.

Responsibilities

Lead the Performance Engineering Center of Excellence by defining performance standards, reusable frameworks, tooling strategy, governance practices, and scalable enablement models.
Design realistic workload models, performance baselines, SLAs, and regression thresholds informed by production metrics, usage analytics, clinical transaction patterns, concurrency targets, throughput goals, and data volumes.
Plan and execute load, stress, spike, soak, capacity, and scalability testing for high-volume workflows, and communicate engineering recommendations and remediation priorities.
Partner with development, architecture, DevOps, and database teams to identify and resolve bottlenecks across APIs, JVM, databases, caching, frontend, network, infrastructure, and cloud layers.
Define automation coverage targets and guide scalable frameworks using Selenium, Playwright, RestAssured, or equivalent tools; use coverage thresholds in release quality gates.
Establish and track quality metrics for performance, scalability, reliability, security, accessibility, environment stability, release confidence, defect escape rate, and other non-functional requirements.
Lead release-readiness and QA governance reviews, including entry and exit criteria, test strategy alignment, go/no-go recommendations, defect triage, test evidence, release process adherence, and risk communication.
Improve QA productivity by reducing test cycle time, increasing defect detection efficiency, improving MTTR and sprint predictability, stabilizing environments, and reducing flaky automation.
Partner with product, engineering, architecture, DevOps, and QA leaders in planning, design reviews, defect triage, release readiness, and product discussions to surface risks and influence decisions with data.
Support the Testing CoE by sharing reusable frameworks, standards, automation assets, lessons learned, and best practices.
Lead QA strategy, test planning, QA and automation maturity, release sign-off, quality metrics, non-functional requirements governance, process improvement, and stakeholder communication across product portfolios.