Junior data engineer
Pubblicato il 07-10-2026 - Cato in Milano
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.
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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. Experience 1-2 years writing P