{"id":577102,"url":"https://alion.io/job/irth-solutions-quality-analyst","title":"Quality Analyst","company":{"id":7550,"name":"Irth Solutions","domain":"irthsolutions.com","url":"https://alion.io/company/irth-solutions","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":95,"open_postings":4,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":60,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Analytics","role_family":"Analytics","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":65000,"max_usd":147000,"period":"year","method":"role_country_seniority_unknown","sample_n":5687},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Cypress","optional":false},{"name":"Databricks","optional":false},{"name":"Git","optional":false},{"name":"JavaScript","optional":false},{"name":"Playwright","optional":false},{"name":"Postman","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Rest-Assured","optional":false},{"name":"Selenium","optional":false},{"name":"SQL","optional":false},{"name":"AI Agents","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Claude Code","optional":true},{"name":"Copilot","optional":true},{"name":"Cursor","optional":true},{"name":"ETL/ELT","optional":true},{"name":"GitHub Actions","optional":true},{"name":"JMeter","optional":true},{"name":"k6","optional":true},{"name":"Locust","optional":true},{"name":"Machine Learning","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-14T00:00:00Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-30T01:18:04Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":15},"description":"QA Engineer - Insights (AI/ML)\nLocation: Remote (US)\nDepartment: Insights (AI/ML)\nReports to: Engineering Manager\nAbout the Role\nIrth is building a new AI-driven threat and risk management platform for pipeline asset integrity. The platform brings together three capabilities that have historically been separate at Irth:\nA governed, cross-product data platform built on Databricks and Azure\nAn AI-powered ingestion layer that normalizes, repairs, and enriches customer data without services-heavy onboarding\nA reusable analytical layer that runs industry-standard, Irth-developed, and customer-built risk models against the data\nWe are seeking a mid-level QA Engineer to own quality across the platform end to end, including the user interface, APIs and platform services, and the underlying analytics and model layer.\nThis role goes beyond traditional application testing. You will validate that data survives ingestion and transformation intact, risk-model outputs are accurate and reproducible, and the evidence trail behind those outputs can withstand regulatory audit. Operators use these outputs to prioritize excavation and repair work, so a silently incorrect result can be far more consequential than a visibly broken interface.\nYou will own regression coverage and test automation while helping build quality into the delivery pipeline from the beginning rather than inspecting it at the end.\nKey Responsibilities\n1. End-to-End Test Ownership - Primary Responsibility\nOwn the end-to-end test strategy across the user interface, APIs, and analytics layers, defining the appropriate coverage at each level.\nDesign, build, and maintain automated test suites covering UI, API contracts and integrations, data validation, and model validation.\nBuild and maintain regression coverage that runs on every change and provides a reliable signal for release readiness.\nCreate and manage test data, including realistic messy inputs that reflect the quality and variability of actual customer data.\nPerform exploratory testing on new functionality to identify failures that scripted tests may not detect.\n2. Data & Model Validation\nValidate data accuracy throughout ingestion and transformation, including row-level and field-level reconciliation, schema conformance, and completeness checks across Bronze, Silver, and Gold layers.\nValidate model outputs against expected results, known baselines, and golden datasets, including verification that identical inputs produce consistent and reproducible outputs.\nTest edge cases throughout the risk-calculation path, including missing attributes, boundary values, unusual segment geometry, and conflicting source records.\nValidate geospatial correctness, including alignment of results to pipeline centerline geometry and consequence-area assignment.\nVerify AI-assisted extraction and gap-filling workflows, ensuring low-confidence outputs are routed for human review rather than silently accepted.\n3. CI/CD & Test Automation Infrastructure\nIntegrate automated test suites into CI/CD pipelines with appropriate quality gates at each environment promotion.\nOwn test-environment configuration, data seeding, and teardown, including infrastructure-as-code contributions where appropriate.\nBuild and maintain the test automation framework, keeping suites fast, stable, maintainable, and trustworthy. Treat flaky tests as defects.\nReport quality signals to the team, including coverage, pass rates, defect escape rate, and quality trends over time.\n4. Release Readiness & Non-Functional Testing\nCoordinate release testing and sign-off, including assessing what changed and what functionality could plausibly be affected.\nSupport performance and load testing of APIs and analytical workloads ahead of release milestones.\nSupport security testing activities, including access-control verification and tenant-isolation testing.\nVerify upgrade and migration paths, including data migrations from legacy formats and systems.\n5. Audit-Defensible Test Evidence\nProduce and retain test evidence sufficient to support regulatory and audit review of risk-model outputs.\nMaintain traceability from requirements through test cases and execution results.\nDocument validation approaches, test results, and known limitations for each model release.\n6. Collaboration & Quality Culture\nWork with Product to translate requirements into clear, concrete, and testable acceptance criteria before development begins.\nPartner with engineers on testability, unit and integration coverage, and defect triage.\nAdvocate for quality during design reviews and identify risks early rather than waiting until the end of a sprint.\nMaintain clear defect-management practices, including reproducible steps, severity assessment, impact analysis, and verification of fixes.\nRequirements\nQualifications\nRequired Qualifications\n3-5 years of software quality assurance experience spanning both manual and automated testing.\nHands-on test automation experience across UI and API layers using tools such as Playwright, Cypress, Selenium, Postman, or REST Assured.\nStrong SQL skills and the ability to independently validate data rather than relying solely on application-level reporting.\nScripting or programming proficiency, ideally in Python or JavaScript, sufficient to build and maintain an automation framework.\nDemonstrable CI/CD experience, including integrating automated tests into pipelines, implementing quality gates, and supporting environment promotion.\nExperience testing REST APIs, including contract testing, authentication, error handling, and asynchronous workflows.\nExperience with test planning, test-case design, defect management, and release sign-off.\nWorking knowledge of Git and branch-based development workflows.\nStrong written communication skills, with the ability to create clear defect reports and test documentation that others can act on.\nPreferred Qualifications\nExperience testing data pipelines, ETL processes, or analytics platforms, including data reconciliation and data-quality validation.\nExperience validating machine learning or statistical model outputs, including reproducibility and model regression testing.\nExperience with Databricks, Spark, or modern lakehouse platforms.\nPerformance and load testing experience using tools such as JMeter, k6, or Locust.\nExperience with cloud platforms; Azure preferred, including Azure DevOps or GitHub Actions.\nExperience testing multi-tenant SaaS applications, including tenant isolation and access-control verification.\nFamiliarity with geospatial data validation and testing.\nExperience using AI-assisted coding tools such as Cursor or GitHub Copilot and/or agentic coding tools such as Claude Code as part of a professional development workflow, including authoring and maintaining automated tests.\nNice to Have\nExperience working in a regulated industry where test evidence is subject to external audit.\nUnderstanding of pipeline integrity or asset-management concepts.\nExperience with security testing practices, including static and dynamic analysis tools.\nExperience validating data migrations from legacy or spreadsheet-based systems.\nExperience with accessibility testing.\nSuccess Metrics\nSuccess in this role will be measured by:\nAutomated regression coverage across UI, API, and analytics layers that runs reliably in CI on every change.\nLow defect escape rates, with critical defects identified and resolved before reaching production.\nData and model validation processes that identify accuracy issues before they reach customers.\nAudit-defensible test evidence maintained for every model release.\nStable, fast, and trustworthy test suites that the engineering team relies on rather than routinely bypassing or overriding.\nQuality risks identified and communicated early enough in the development cycle to be addressed proactively.\nBenefits\nWhat We Offer\nJoin a dynamic, growing company with a strong reputation in its industry.\nCompetitive salary.\nComprehensive health plan options, including medical, dental, and vision coverage.\n401(k) plan with company match.\nFlexible PTO policy plus company-paid holidays.\nAdditional benefits, including life insurance, pet insurance, and employee discounts and perks programs.\nGenerous one-time work-from-home stipend to help you set up your home workspace.\nCompany events and opportunities to connect, including monthly team lunches, volunteer outings, and quarterly gatherings.\nHybrid employees have access to complimentary snacks, beverages, and coffee at our Columbus, Ohio office.","description_format":"text","description_chars":8522,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Company events","Life insurance","Professional development"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Low-Code & No-Code Platforms","Field Service Management 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