About the role
We're growing the team that keeps our data infrastructure running and our archive growing. This is hands-on work at the intersection of Python, Django, and databases at real scale.
You will:
Maintain and extend the hundreds of scrapers that continuously pull court data into our system
Ingest millions of documents and merge them safely into our production database
Help build and evolve the portal receiving our digitization work at the United States Library of Congress, where a team is scanning millions of pages
Convert scanned material into structured text and documents, and merge it into the archive without breaking what's already there
Work on the machine learning processes that identify and remove copyrighted material - a critical safeguard for a nonprofit like ours
Design batch operations that run across five to ten million records efficiently, without taking the databases down
Diagnose slow queries, review migrations, and be a person the team can rely on when something in the database needs a second pair of eyes
Hard requirements
Please don't apply if you don't have real experience in these areas:
Python, professionally and at depth
Django - this is the framework we build on and the single most important skill for this role
Strong database experience. We run Postgres, but we care more about your fundamentals than the specific engine: you should be comfortable reading an execution plan, tracking down why a query is slow, reasoning about indexes, and handling migrations that don't go as planned
Web scraping - building, maintaining, and debugging scrapers in production
Nice to have
Celery, Redis, Kubernetes
Machine learning applied to document processing (OCR, classification, extraction)
Experience with legal data, civic tech, govtech, or open data
Elasticsearch or similar search infrastructure
A genuine interest in law, access to justice, or social justice work — it's what we do every day
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