Sr. Software QA Engineer

Entrust•Shakopee, MN

About The Position

Entrust is hiring a Senior Quality Assurance Automation Engineer to work with multiple product teams to validate the quality, security, and scalability of user interfaces, web services, APIs, and distributed enterprise workflows. The role designs and executes end-to-end automated tests, benchmarks system behavior, analyzes results, identifies bottlenecks, and improves test suites for performance and reliability. The engineer collaborates with software development partners to define test strategies, expand automation, and use approved AI-assisted engineering tools such as GitHub Copilot with strong human oversight. The position includes automated deployment, setup, and system testing across software and hardware products; configuring cloud and on-premises environments for supported scenarios; and testing LDAP, database, network, performance, and load configurations. It also involves developing reusable automation and QA processes that connect requirements, code changes, issues, test results, logs, and documentation, and validating and documenting AI-assisted outputs where they influence test assets or release readiness. Entrust offers career development, flexible work options, and a collaborative, inclusive culture. The role’s anticipated starting base pay is $91,619–$134,375 per year in the primary posting location and is eligible for the company’s discretionary annual incentive plan. Eligible colleagues and dependents may receive medical, vision, and dental coverage; 401(k) matching; life and disability insurance; mental health coaching; virtual fitness programs; paid personal time off and 12 paid holidays; parental leave; and education reimbursement. Benefits and compensation are subject to eligibility and plan terms.

Requirements

At least 6 years of relevant experience in Test/Quality Assurance, IT Infrastructure, or Support.
At least 3 years of experience developing automated tests with frameworks such as RestAssured, Selenium, and/or Playwright.
Programming experience in JavaScript/TypeScript, Java, PowerShell, and/or C#.
Practical experience using GitHub Copilot or comparable approved AI-assisted coding tools for test automation, code review assistance, debugging support, documentation, and productivity improvement.
Ability to write clear prompts, provide relevant code and test context, evaluate AI-generated suggestions, and identify when manual QA judgment is required.
Experience with Agile/Scrum practices and understanding of the QA role and responsibilities in an Agile development environment.
Cryptography knowledge, including key management.
Unrestricted work authorization in the U.S.

Nice To Haves

BS/BA in Computer Science, Engineering, or equivalent experience.
Ansible experience scripting deployments from development to test to production.
Experience with Git, GitHub, and/or Bitbucket for source code control.
Experience with Jira and/or Microsoft Azure DevOps Server for user-story coordination, test-case management, build integration, and issue tracking.
Experience integrating AI-assisted QA workflows with pull requests, design documentation, Jira, Azure DevOps, CI/CD pipelines, logs, virtual machines, or test reporting.
Familiarity with responsible AI practices, including approved-tool usage, sensitive-data handling, intellectual-property awareness, secure coding guardrails, and review of AI-generated automation before reuse.

Responsibilities

Author automated test cases using frameworks such as Playwright with TypeScript, Selenium, and related API-testing tools.
Automate deployment, setup, and system testing of solutions across software and hardware products.
Configure cloud and on-premises environments to verify supported solution scenarios, including LDAP, database, and network configurations.
Potentially automate the creation of different environments, including single and distributed deployments.
Test products end to end, including performance and load testing.
Create and report test-cycle metrics, identify bottlenecks, and recommend improvements that reduce cycle time.
Collaborate daily with project management, technical leads, and developers, providing status on current and upcoming issues and possible future enhancements.
Use approved AI-assisted tools, including GitHub Copilot, for test-case generation, automation scripting, pull-request analysis, documentation, and regression impact assessment.
Create reusable automation workflows and prompts that connect product requirements, code changes, Jira or Azure DevOps issues, test results, logs, and documentation into repeatable QA processes.
Validate AI-generated output for correctness, security, privacy, completeness, and alignment with product behavior before incorporating it into test assets or automation suites.
Improve automated test coverage, regression readiness, and feedback speed by identifying repeatable QA workflows and reducing manual effort across release cycles.
Document how AI-assisted outputs were reviewed, validated, and incorporated when they influence test coverage, quality decisions, or product release readiness.