Job Description

and Mission

We are looking for a SDET to help modernize quality engineering for our Accident Management platform. This is a hands-on engineering role focused on test strategy, automation-ready test case design, release confidence, and close partnership with product and software engineering.

You will report into the Accident Management engineering leadership team and work directly with front-end, backend, QA, automation, product, and support partners. You will join grooming, design, and delivery meetings; ask the quality, edge-case, observability, and release-readiness questions early; and translate product intent into clear acceptance scenarios, test cases, automation plans, and measurable release gates.

This is not a manual QA role and not an automation-only role. We already have QA automation engineers who can execute against well-defined automation work. Your job is to create the quality engineering system around them: clarify what needs to be tested, where it should be tested, what evidence proves readiness, how automation should be structured, and how engineering can prevent issues before QA becomes the last line of defense.

The ideal candidate is a software engineer who specializes in quality: someone who can write and review automation code, design frameworks, understand API and UI behavior, read logs, work through CI failures, write excellent test cases, and influence product and engineering decisions before problems reach production.

What Success Looks Like:

In the first 30 days, you understand the Accident Management product flows, existing QA process, automation assets, release process, and current pain points. You can participate in grooming, identify gaps in acceptance criteria, run or inspect automated tests, and begin shaping higher-quality test cases for active work.

In the first 60 days, you are regularly joining engineering/product meetings, writing automation-ready test cases, feeding high-quality work to QA automation engineers, reviewing automation PRs, and improving release-readiness evidence for at least one critical workflow.

In the first 90 days, you are operating as a quality engineering counterpart to engineering leadership: reducing ambiguity before development starts, helping developers own quality earlier, making automation work more predictable for QA automation partners, improving CI/test signal, and reducing avoidable staging and production surprises.

Key Outcomes

• Own quality strategy for Accident Management features from product intake through production readiness.
• Join product grooming, engineering design, sprint planning, and release-readiness meetings as the onshore quality engineering lead.
• Ask direct questions about edge cases, acceptance criteria, test data, observability, rollback, client impact, and success metrics.
• Convert ambiguous product requirements into plain-English acceptance scenarios and automation-ready test cases.
• Write test cases with clear preconditions, steps, data setup, expected results, priority, tags, and traceability back to Jira.
• Partner with QA automation engineers in South America to turn test cases into Playwright/API automation and reliable regression coverage.
• Review automation PRs and framework changes for maintainability, reliability, test layering, and signal quality.
• Define when behavior belongs in unit, component, API, contract, synthetic, or end-to-end testing.
• Strengthen test management and reporting through Zephyr, TestRail, Xray, Allure, or similar tooling.
• Improve CI/CD quality signals with GitHub Actions, automated reporting, historical run tracking, and actionable failure triage.
• Partner with developers to design testable software, build test harnesses, mock integrations, and reduce manual validation burden.
• Define release-readiness evidence for staging and production launches, including smoke/regression scope, logs, dashboards, monitoring, synthetic checks, rollback criteria, and UAT/client evidence.
• Debug production-like issues across UI, APIs, authentication, environment configuration, test data, integrations, logs, and monitoring tools.
• Mentor developers and QA partners on scenario-driven testing, quality-first engineering, automation patterns, and practical AI-assisted test generation.
• Help the team move from reactive QA at the end of delivery to quality engineering embedded throughout the SDLC.

Skills, Education and Experience

• Bachelors Degree - Computer Science or related field
• 4+ years of professional software engineering, SDET, or quality engineering experience.
• Strong hands-on automation experience across UI and API layers, preferably with Playwright, Cypress, Selenium, pytest, or similar tools.
• Ability to write production-quality test automation and review automation/framework code written by others.
• Experience designing test frameworks, test data strategies, tagging conventions, CI gates, and regression/smoke suites.
• Strong ability to write clear, executable test cases and acceptance scenarios for complex business workflows.
• Engineering fluency across front-end applications, backend APIs, service contracts, authentication, logs, CI/CD, and observability.
• Ability to join product and engineering meetings and ask useful questions before requirements become code.
• Practical experience with Jira-linked test management tools such as Zephyr, TestRail, Xray, Allure, or equivalent systems.
• Strong debugging skills: CI failures, flaky tests, environment mismatches, data issues, API failures, authentication issues, and production-like incidents.
• Ability to balance speed and rigor: know what must be automated now, what can stay manual temporarily, and what evidence is enough for release confidence.
• Strong communication skills with product, engineering, QA automation, manual QA, offshore/nearshore teams, support, and leadership.
• Willingness to be hands-on while also leading process: this person should not only coordinate quality work, but be able to do the work and raise the bar

Strong Plus to Have:

• Angular, TypeScript, Nx, monorepo, or modern front-end architecture experience.
• Python experience for API validation, test tooling, data setup, or internal automation utilities.
• AWS, Cognito/authentication, CloudWatch, Datadog, Snowflake, Splunk, or similar observability/data platform exposure.
• Experience building synthetic monitors, contract tests, API harnesses, or data-driven regression suites.
• Experience integrating automated test results into Jira/test management workflows.
• Experience helping a team modernize from spreadsheet/manual QA toward traceable, automated, CI-visible coverage.
• Experience in insurance, claims, dispatch, logistics, contact center, or other workflow-heavy enterprise products.
• Experience using AI coding or test-generation tools responsibly inside a review-heavy engineering process.

Hiring In:

• United States: Arizona, California, Florida, Georgia, Illinois, Massachusetts, Michigan, New Hampshire, New Mexico, New York State, North Carolina, Tennessee, Virginia

The anticipated closing date to submit applications for this role is 8/15/2026.Join our Greenhouse Candidate Portal to track your application status and receive instant alerts for future openings.