Automation Test Engineering

Lead the Way to Intelligent Banking with Us!You might not think about what happens behind the scenes when you check your bank balance or deposit a check from your phone, but we do. Kinective empowers banks and credit unions to move beyond keeping up with technology to shaping the future of banking. Our platform seamlessly connects the right tools, delivers real-time data, and drives smarter operations for more than 4,000 financial institutions nationwide. At Kinective, we are leading the way to intelligent banking together and enjoying the journey along the way.Why This Role MattersAs a Manual Test & Automation Engineer at Kinective, you'll play a key role in ensuring our products are reliable, scalable, and ready for real-world banking environments. You'll own quality engineering for assigned product areas while contributing to both hands-on manual testing and automation efforts, helping strengthen release confidence, AI-assisted testing, and technical quality practices across the Data Intelligence platform.This role goes beyond test execution—you'll translate requirements into thoughtful test strategies, perform manual testing as needed, build maintainable UI and API automation, design and govern AI-assisted QA workflows, improve framework and pipeline health, make risk visible, and partner with Product and Engineering to deliver with confidence. If you enjoy digging into complex problems, improving how things work, and building software customers can trust, you'll thrive here. You'll work primarily in a data-heavy enterprise platform with a mature, component-framework-based UI — dynamic grids, embedded frames, and heavy asynchronous loading. Building automation that stays stable against it is genuinely challenging work, and genuinely valuable when it's done well.What You'll OwnTest Strategy & PlanningOwn risk-based test strategy for assigned product areas, translating requirements and acceptance criteria into clear, testable outcomes.Drive coverage planning across scenarios, test data, environments, and release risks in partnership with QA, Product, and Engineering.Use AI-assisted story analysis to accelerate test design while ensuring human review, traceability, and quality standards.Test Execution & AutomationPerform manual functional, regression, exploratory, and scenario-based testing as needed to validate product quality and release readiness.Own reliable UI and API automation for critical workflows using Playwright, TypeScript, and maintainable test patterns.Run and report release-scoped regression, producing clear readiness evidence and a documented view of coverage gaps before a release.Build reusable page objects, components, utilities, fixtures, test data, assertions, and diagnostics that improve speed and trust.Apply AI-assisted tools to generate, extend, debug, and maintain tests while validating correctness, security, determinism, and release relevance.Manage test environments and test data, including provisioning and refreshing environments, loading and validating datasets, and troubleshooting remote access and connectivity issues.Diagnose root causes across application, test, data, environment, and infrastructure issues using evidence-driven analysis.Turn escaped defects and production incidents into durable regression coverage or a documented improvement plan.Collaboration & Continuous ImprovementOwn quality visibility across the SDLC by surfacing regression trends, flaky-test patterns, framework health, CI/CD feedback, and release evidence.Own Jira/Xray test-management upkeep on a per-story basis: creating Test issues, linking Test Executions back to Stories, and keeping automation-to-test-key mappings current so traceability stays intact.Use and maintain the team's AI-assisted QA workflows, applying judgment about when generated output can be trusted and when it needs rework.Partner across teams, review test code, mentor others, and raise quality standards through shared ownership and practical improvements.How We'll Measure SuccessHigh-quality releases with reliable evidence and minimal critical defects reaching production.Strong risk-based coverage, including effective automation that improves speed, confidence, and repeatability.AI-assisted QA workflows improve speed and coverage while producing standards-compliant, maintainable, reviewable, and traceable outputs.Ongoing improvements to QA processes, framework health, pipeline feedback, and release readiness.Success here isn't about checking boxes – it's about propelling Kinective's mission to lead the way to intelligent banking. Necessary Qualifications & Competencies4+ years in Quality Assurance, Quality Engineering, manual testing, automation engineering, or a similar role.2+ years designing and maintaining test automation for UI, API, and regression coverage.Practical experience using AI-assisted tools in QA or software engineering workflows, including test design, code generation, debugging, failure analysis, or documentation.Experience working as a senior individual contributor, technical owner, or mentor in a quality engineering team.Technical SkillsHands-on experience with Playwright and TypeScript or a comparable modern browser-automation framework.Ability to evaluate AI-generated outputs for correctness, maintainability, traceability, security, deterministic behavior, and fit-for-purpose coverage.Experience with API testing tools such as REST Assured, Postman, or similar.Working knowledge of SQL, APIs, GitLab, pull requests, code review, CI/CD pipelines, Jira, Xray, and system integrations.Process & ToolsStrong understanding of SDLC, STLC, Agile delivery, risk-based testing, and shift-left quality practices.Ability to design maintainable automation using page objects, reusable components, stable locators, meaningful assertions, and useful diagnostics.Ability to analyze logs, traces, screenshots, AI-generated explanations, and test artifacts to identify root cause and improve test signal.Core CompetenciesStrong analytical and problem-solving skills with high attention to detail and sound judgment when using AI-generated recommendations.Clear documentation, communication, and stakeholder-management skills, including the ability to explain AI-assisted QA decisions and tradeoffs.Self-motivated, adaptable, collaborative, and comfortable influencing responsible AI adoption without formal authority.Preferred SkillsFamiliarity with Selenium/WebDriver and Java-based legacy automation is a plus, but not required.Experience testing complex single-page applications, dynamic component libraries, or data-heavy enterprise systems.Experience with enterprise CI/CD and DevOps platforms, containerized test execution, debugging and trace analysis, test reporting, and quality engineering dashboards.Experience evaluating AI-enabled product behavior, LLM-based features, confidence thresholds, model outputs, or human-review workflows.Experience testing data integrity, permissions, security boundaries, reporting, synchronization, or AI-enabled product behavior.Experience designing or operating AI-assisted or agentic QA workflows that connect requirements, test generation, execution, failure analysis, and test-management updates.Familiarity with financial services data — banking or credit union systems, core integrations, or regulated reporting.