Performance Engineering - Sr. QA Manager

McKesson•Irving, TX

9h•Hybrid

About The Position

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you. Position Summary We are 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. This role is ideal for a technical leader who can improve the reliability, scalability, and performance of EMR/EHR systems while mentoring strong QA teams and influencing engineering decisions with data. The candidate 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.

Requirements

12+ years of progressive Quality Engineering and/or Performance Engineering experience, including 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 without relying solely on team execution.
Hands-on experience with JMeter, LoadRunner, k6, Gatling, NeoLoad, or equivalent tools, 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 modern 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 platforms, with 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, Redis, query tuning, and indexing.
Strong troubleshooting capability across application code, JVM, database, network, infrastructure, and frontend layers, with the 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 test 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.

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 modern cloud platforms such as AWS, Azure, or GCP.
Knowledge of healthcare interoperability standards such as HL7 and FHIR, along with awareness of healthcare data sensitivity, HIPAA, audit readiness, and collaboration with compliance, security, and legal partners.

Responsibilities

Lead the Performance Engineering CoE by defining performance standards, reusable frameworks, tooling strategy, governance practices, and enablement models that scale across product lines.
Design realistic workload models, performance baselines, SLAs, and regression thresholds using 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, then translate results into clear 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, guide scalable frameworks using Selenium, Playwright, RestAssured, or equivalent tools, and use coverage thresholds as part of release quality gates.
Establish and track quality metrics across performance, scalability, reliability, security, accessibility, environment stability, release confidence, defect escape rate, and other non-functional requirements.
Lead release readiness and QA governance reviews with clear entry and exit criteria, test strategy alignment, go/no-go recommendations, defect triage discipline, test evidence, release process adherence, and transparent risk communication.
Improve QA productivity by reducing test cycle time, increasing defect detection efficiency, improving MTTR, strengthening sprint predictability, stabilizing environments, and reducing flaky automation.
Partner with product, engineering, architecture, DevOps, and QA leaders during planning, design reviews, defect triage, release readiness, and product discussions to surface risks early and influence decisions with data.
Support the Testing CoE by sharing reusable frameworks, standards, automation assets, lessons learned, and best practices that raise quality maturity across the organization.