Quality Engineer

The role

Quality at Litehouse goes beyond checking screens before a release. It means our operator-facing web app works as expected, GraphQL and service APIs return the right data to the right tenant, and ticketing, POS, and engagement records flow accurately into analytics and digests. We are looking for a Quality Engineer who asks what could go wrong and builds practical, repeatable ways to catch it early across development, staging, and production.

This is a hands-on engineering role and the first dedicated quality seat on a small team. Engineers already care about quality, but ownership of the practice, automation, and release confidence sits with this role. You will not start from zero. You will take the automation and release checks we already have, make them reliable, and expand them where risk is highest.

You will write test code, keep CI quality gates useful, investigate failures in Datadog, and work with product and engineering from design through release. We already use Claude Code and Cursor in day-to-day development. Part of this job is building the checks and harnesses that keep low-quality or unverified work, from people or AI tools, out of the product.

What you’ll do

• Own hands-on QA across web, mobile, APIs, and integrations, including exploratory, functional, regression, and release testing
• Build and maintain reliable test automation and reusable infrastructure, including fixtures, test data, mocks, and environment setup
• Add meaningful quality gates to GitHub Actions so the team receives fast, useful feedback before changes are merged
• Keep the test suite healthy by investigating flaky tests, improving failure reporting, and removing low-value checks
• Validate data from source ingestion through transformation, storage, and APIs, with automated checks for schema drift, missing or duplicated records, incorrect transformations, and anomalies
• Test permissions and multi-tenant isolation so roles only see what they should, and one organization's data never leaks into another
• Help make promotion across environments safer by checking that migrations, seeds, config, and critical journeys stay aligned from development through staging and production
• Work with engineers and product partners to make requirements testable and identify risk before development begins
• Use Datadog logs, traces, real user monitoring, dashboards, and alerts to investigate failures and turn production signals into regression coverage
• Design repeatable test harnesses for coding agents such as Claude Code and Cursor, with controlled environments, seeded data, clear assertions, appropriate tool permissions, and observable results
• Evaluate AI-generated code and tests for correctness, coverage, and maintainability, and build regression suites for agent workflows
• Give the team clear, actionable feedback on defects and risks, and help engineers strengthen quality in their everyday work

What we’re looking for

• 4+ years of experience in quality engineering, test automation, SDET, or a similar software engineering role
• Strong TypeScript or Python skills and a commitment to treating test code like production code
• Experience building browser automation with Playwright, Cypress, Puppeteer, Selenium, or a similar framework
• Solid SQL skills and experience validating data across systems or pipeline stages
• Experience testing APIs and diagnosing failures across service boundaries; GraphQL experience is a strong plus
• Experience integrating automated tests into GitHub Actions or another CI/CD platform
• Hands-on experience using observability to investigate failures and guide test coverage; Datadog experience is strongly preferred
• Sound judgment about what to automate, what to test manually, and how deeply to test based on risk
• Clear communication skills and a collaborative approach to improving quality across a team

Nice to have

• Hands-on experience incorporating Claude Code, Cursor, or similar coding agents into engineering and testing workflows
• Mobile testing experience, especially with React Native applications
• Experience testing modern data platforms such as Databricks, Snowflake, BigQuery, or Redshift, and tools such as dbt, Spark, or Airflow
• Experience testing and troubleshooting applications deployed to Microsoft Azure and Kubernetes
• Familiarity with data quality frameworks such as Great Expectations, Soda, dbt tests, Deequ, or similar tooling
• Experience testing AI or LLM product features, including output quality, groundedness, prompt regressions, tool use, and safety guardrails
• Familiarity with ticketing, point-of-sale, access control, or other venue technology
• Experience helping a growing engineering organization establish its quality practices

Benefits and Working Environment

• Flexible paid time off
• Support for professional development, including certification costs and relevant training.
• Remote-first, distributed team with an emphasis on clear communication and documentation over meetings.
• Meaningful ownership of a function you're building largely from scratch