Sobre esta vaga de QA Engineer (Remote) na Trellis
Trellis (https://trellis.law/) is the largest legal research platform for state trial courts, with coverage spanning thousands of courts and millions of cases across the U.S. We aggregate and structure trial court filings, rulings, motions, and outcomes into a unified, searchable platform, combining large-scale data infrastructure with AI-powered analysis to extract, organize, and surface insights across that data. This makes large-scale trial court research possible for the first time.
Our tools enable attorneys to analyze judge tendencies, evaluate opposing counsel, find and assess expert witnesses, and see which arguments and strategies have succeeded in similar cases, so they can assess risk earlier, save time on research, and develop more effective, data-backed case strategies.
We’re revolutionizing the way legal professionals access and use trial court data to make informed decisions.
About the Role:
We're looking for a QA Engineer who approaches quality as an engineering problem. This is NOT just a manual testing role — it's for an engineer who writes automation to proactively find bugs, surface data quality issues at scale, and then writes or contributes code to fix them.
You'll work closely with engineering and product to build systems that continuously monitor data integrity and product behavior, surface issues based on business impact, and drive resolution. The ideal candidate is a strong programmer who is comfortable working across large datasets, debugging complex pipelines, and owning quality end-to-end: from detection to fix.
We want someone who thinks like a software engineer first and a quality advocate second: someone who sees a data anomaly or product bug and immediately thinks "how do I build something that catches this automatically — and then fixes it?" You should be energized by ambiguity, comfortable digging into large datasets and production logs, and able to move quickly without waiting to be handed a spec.
Our tech stack includes Python, Django, Postgres, Elasticsearch, Vue, vanilla JavaScript, and AWS.
What You'll Do:
Write scripts and tooling to detect anomalies, inconsistencies, or failures in large datasets (e.g. malformed records, missing relationships, unexpected distributions).
Word 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.
