Senior Software Development Engineer in Test

Huron helps its clients drive growth, enhance performance and sustain leadership in the markets they serve. We collaborate with education organizations to develop strategies and implement solutions that enable the transformative change our clients need to own their future. Together, we empower clients to drive innovation, create sustainable funding streams and deliver better student outcomes.
Today, leaders of higher education institutions and academic medical centers are spending too much time reacting to market forces, rather than focusing on the essential priorities that help students, faculty and staff thrive. The most productive path forward requires an intentional approach and innovative thinking, whereby stakeholders across the entire institution rally around a shared vision and embrace the hard work of effecting change.
You’ll help our clients to achieve organizational effectiveness, improve student outcomes, implement new technologies and align resources and investments to ensure long-term sustainability.
Join our team as the expert you are now and create your future.

Primary responsibilities

Test strategy and technical leadership

·       Define and own the test strategy, framework architecture, and tooling roadmap for assigned product areas.

·       Translate business requirements into traceable test cases across functional, regression, integration, and end-to-end dimensions; lead test plan reviews with developers, product owners, and project managers.

·       Define test coverage metrics and quality gates; produce risk-based release readiness reports for engineering leadership at each milestone.

Test automation and AI-assisted engineering

·       AI-assisted development: Leverage GitHub Copilot, Claude Code, and comparable AI coding assistants to accelerate test script authorship, generate edge-case scenarios, and perform intelligent code review.

·       AI agentic testing: Configure and evaluate agentic workflows for autonomous test generation and self-healing locators; apply Model Context Protocol (MCP) to connect AI agents to test tools, APIs, and data sources for end-to-end agentic test workflows; define system instructions, skills, and tool configurations for AI coding agents.

·       Architect and maintain scalable automation frameworks using Selenium, Playwright, or Cypress for UI; REST Assured or similar for APIs; integrate into CI/CD pipelines via Azure DevOps or GitHub Actions.

·       Write complex SQL queries to validate backend data integrity in PostgreSQL (using DBeaver); build data-driven test suites for parallel and cross-browser execution.

Defect management, collaboration, and mentorship

·       Isolate, document, and track defects in Azure DevOps; lead exploratory testing sessions and root-cause analysis on escaped defects.

·       Serve as quality engineering lead across Agile delivery teams; participate in sprint planning, backlog grooming, standups, and retrospectives.

·       Conduct code reviews and coach developers and team members on shift-left testing practices and quality engineering standards.

Required qualifications

Core

·       B.E./B.Tech. in Computer Science, MIS, or related discipline; 4 to 7 years in software quality engineering with the majority in test automation and framework development on a commercial product.

·       Production-quality automation code in C#, Java, Python, or JavaScript/TypeScript; proficiency with Selenium, Playwright, or Cypress; API testing with REST Assured, Postman, or SoapUI.

·       CI/CD pipeline experience (Azure DevOps, GitHub Actions, or Jenkins); strong Git skills; cross-browser testing with BrowserStack or equivalent; solid SQL skills for database validation.

·       Deep understanding of QA lifecycle and Agile/Iterative delivery; clear communicator able to produce test strategies and quality summaries for both technical and non-technical audiences.

·       Demonstrated experience with performance engineering, scalability design, and observability practices (logging, metrics, distributed tracing) in production systems.

·       Hands-on experience with AWS services including Lambda, ECS, RDS, API Gateway, CloudWatch, and related cloud-native tooling.

AI and agentic development tools

·       Practical daily use of AI coding assistants (GitHub Copilot, Claude Code, or Cursor) to write, refactor, and review test code; proven ability to craft effective prompts and system instructions.

·       Solid understanding of AI agents, skills, and tool-calling; familiarity with Model Context Protocol (MCP) — ability to connect AI models to internal tools, APIs, and test infrastructure for multi-step agentic workflows; working knowledge of an LLM API or orchestration framework (Anthropic API, LangChain, or similar) is a strong plus.

Preferred qualifications

·       Performance and/or security testing (JMeter, k6, OWASP ZAP); TDD/BDD methodologies (Cucumber, Reqnroll); containerized test environments (Docker, Kubernetes).

·       Working knowledge of JavaScript/TypeScript; familiarity with Visual Studio and the Microsoft development ecosystem; ISTQB certification or equivalent credential.

·       Knowledge of AWS services (SNS, S3, EC2, Lambda, etc.) is an advantage; familiarity with cloud-based test environment provisioning and infrastructure as code.