15 set - Roma
Cato
Your missionBring a tender from the source portal into Cato: scraping, parsing, merging, enrichment. You'll start by owning a handful of sources end to end - the scraper, the job behind it, and the data that comes out - and take on more as you go. Not tickets handed to you: sources you're responsible for.
What you'll actually doBuild and maintain scrapers for national tender portals, where reading the source in its original language is part of the job.
Keep them alive: portals change their HTML, move endpoints, break pagination, throttle you. You find out before the customer does.
Turn messy sources into clean records: broken HTML, inconsistent XML, APIs that lie about their own schema.
Write and maintain orchestrator flows: retries, backfills, alerting, and a clear answer to "Did today's run actually land?"Work on merge and dedup - the same tender arrives three times, in three shapes, and only one version can reach the customer.
Ship AI enrichment steps: batch LLM extraction of requirements, embeddings, OCR on attachments.
Guard data quality with tests and checks that fail loudly before a customer finds the gap.
Ideal profilePython that holds up: typed, tested, and readable six months later.
You've scraped something real: HTTP, HTML and XML parsing, pagination, sessions, rate limits - and you know why a scraper that worked yesterday is broken this morning. SQL you're comfortable in: joins, aggregations, window functions. You'll read from the database every day; tuning and running it isn't your job.
Builder by default: you see a manual process and your first instinct is to automate it. Comfortable with messy sources: broken HTML, inconsistent XML, PDFs that were scans of scans.
You close your own loop: you check that what you shipped actually ran, before someone else has to ask.
Experience1-2 years writing Python in production: scrapers, ETL scripts, automation - anything that had to run unattended and be fixed when it didn't.Exposure to an orchestrator (Prefect, Ai
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