
As districts and their venues grow more complex, visibility and coordination break down across teams and technologies. Data flows from ticketing, access control, facilities, mobility, food and beverage, and tenant platforms—but it remains fragmented and difficult to use when it matters.

Litehouse sits above existing systems of record to unify and contextualize this data into a single operational view, providing clarity into how districts and venues are operating as a system. This enables coordinated decision-making and action across assets and events—without replacing core technologies or increasing operational overhead.

H&S Ventures

Incubator / originating venture studio

Litehouse

Litehouse creates a shared operational view across districts and venues, unifying and contextualizing their data.

Company Overview

Litehouse is a district operating system that brings clarity, coordination, and intelligence to how smart venues and mixed-use districts operate. It's an AI-enabled technology platform that creates a single coordinating layer above a district's existing systems to make them collectively intelligent.

Litehouse was originally incubated within the H&S Ventures portfolio. It was built by operators who have been through the full complexity of what it takes to bring a full-scale multi-use district to life — the technical architecture, the organizational design, the governance debates, the moments where the ambition of the project collides with the reality of execution. That lived experience didn't just inform Litehouse, it led to its creation.

The company is evolving from incubation into a scalable, market-ready platform, with a strong emphasis on platform architecture, operational rigor, and customer-driven innovation.

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 testingBuild and maintain reliable test automation and reusable infrastructure, including fixtures, test data, mocks, and environment setupAdd meaningful quality gates to GitHub Actions so the team receives fast, useful feedback before changes are mergedKeep the test suite healthy by investigating flaky tests, improving failure reporting, and removing low-value checksValidate data from source ingestion through transformation, storage, and APIs, with automated checks for schema drift, missing or duplicated records, incorrect transformations, and anomaliesTest permissions and multi-tenant isolation so roles only see what they should, and one organization's data never leaks into anotherHelp make promotion across environments safer by checking that migrations, seeds, config, and critical journeys stay aligned from development through staging and productionWork with engineers and product partners to make requirements testable and identify risk before development beginsUse Datadog logs, traces, real user monitoring, dashboards, and alerts to investigate failures and turn production signals into regression coverageDesign repeatable test harnesses for coding agents such as Claude Code and Cursor, with controlled environments, seeded data, clear assertions, appropriate tool permissions, and observable resultsEvaluate AI-generated code and tests for correctness, coverage, and maintainability, and build regression suites for agent workflowsGive 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 roleStrong TypeScript or Python skills and a commitment to treating test code like production codeExperience building browser automation with Playwright, Cypress, Puppeteer, Selenium, or a similar frameworkSolid SQL skills and experience validating data across systems or pipeline stagesExperience testing APIs and diagnosing failures across service boundaries; GraphQL experience is a strong plusExperience integrating automated tests into GitHub Actions or another CI/CD platformHands-on experience using observability to investigate failures and guide test coverage; Datadog experience is strongly preferredSound judgment about what to automate, what to test manually, and how deeply to test based on riskClear 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 workflowsMobile testing experience, especially with React Native applicationsExperience testing modern data platforms such as Databricks, Snowflake, BigQuery, or Redshift, and tools such as dbt, Spark, or AirflowExperience testing and troubleshooting applications deployed to Microsoft Azure and KubernetesFamiliarity with data quality frameworks such as Great Expectations, Soda, dbt tests, Deequ, or similar toolingExperience testing AI or LLM product features, including output quality, groundedness, prompt regressions, tool use, and safety guardrailsFamiliarity with ticketing, point-of-sale, access control, or other venue technologyExperience helping a growing engineering organization establish its quality practices

Benefits and Working Environment

Flexible paid time offSupport 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