About Trellis

Trellis is the largest legal research platform for U.S. state trial courts, leveraging large-scale data infrastructure and AI to provide attorneys with powerful insights. Our tools help legal professionals analyze trends, assess risk, and develop data-backed strategies, transforming how legal research is conducted.

About the Role

We are seeking a QA Engineer who approaches quality as an engineering problem. This is not a manual testing role; we are looking for an engineer who writes automation to proactively find bugs and data quality issues at scale, and then contributes code to fix them. You will work closely with engineering and product teams to build systems that continuously monitor data integrity and product behavior, identify issues based on business impact, and drive resolution. The ideal candidate is a strong programmer comfortable working across large datasets, debugging complex pipelines, and owning quality end-to-end.

What You'll Do

• Design and build automated systems that identify data quality issues, product bugs, and regressions across our web application and data pipelines, reducing reliance on manual discovery.
• Write scripts and tooling to detect anomalies, inconsistencies, or failures in large datasets (e.g., malformed records, missing relationships, unexpected distributions).
• Collaborate with product to prioritize identified issues based on business impact, and write or contribute code fixes directly, not just file tickets.
• Develop and maintain automated test coverage (unit, integration, end-to-end) with a focus on high-risk areas and data-heavy workflows.
• Collaborate with engineering to review data models, API contracts, and backend logic for correctness and edge cases.
• Instrument quality metrics and build internal tooling or dashboards to track data health and product reliability over time.
• Contribute to release readiness by running automated checks and surfacing risk areas with supporting data.

Who You Are

• 3+ years of engineering experience with a focus on quality, data integrity, or test automation.
• Strong Python skills — comfortable writing production-quality scripts, automation, and data analysis code.
• Experience working with large datasets: querying, profiling, validating, and debugging data in SQL and NoSQL databases (Postgres/Elasticsearch preferred).
• Proven track record building automated systems that discover bugs or data issues, not just validate known behavior.
• Ability to read, understand, and contribute fixes to a production codebase — this role writes code, not just tests.
• Comfortable working independently and prioritizing a backlog of quality issues by impact.

Nice to Haves

• Experience with Django, Elasticsearch, or Vue.
• Familiarity with AWS-based environments and cloud data pipelines.
• Experience building data quality monitoring, alerting, or observability tooling.
• Familiarity with tools such as Pytest, Great Expectations, dbt tests, Playwright, Postman, or similar.
• Experience as the first or only QA/quality engineer on a team, including setting up processes from scratch.
• Experience working with remote or internationally distributed teams.

Benefits

• Meaningful equity
• Full Medical, dental, and vision coverage (for you and your family). ALL premiums covered!
• 401k w/ full match up to state limit
• Work from anywhere
• Flexible vacation policy. You manage your own time.

Diversity & Inclusion

Trellis is committed to building a diverse and inclusive workplace. If you’re excited about this role but your experience doesn’t align perfectly, we encourage you to apply—you may be the right candidate for this or other opportunities.

AI in Hiring

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.