Your mission Bring a tender from the source portal into Cato: scraping, parsing, merging, enrichment. Youll 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 youre responsible for. What youll actually do * Build 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 todays 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. Adecuado profile * Python that holds up: typed, tested, and readable six months later. * Youve 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 youre comfortable in:



joins, aggregations, window functions. Youll read from the database every day; tuning and running it isnt 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 Python in production: scrapers, ETL scripts, automation - anything that had to run unattended and be fixed when it didnt. * Exposure to an orchestrator (Prefect, Airflow, Dagster) is a plus, not a requirement: youll learn ours properly. * Exposure to LLM-based extraction is welcome; curiosity about it is mandatory. What you wont find here * No micromanagement: we trust you to own your part of the stack. * No "standard" 9-to-5 mentality: we care about outcomes and we are looking for people who are willing to go the extra mile. * No "weve always done it this way" excuses: were here to disrupt, not to follow old patterns. Our Tech Stack * Data & Infra: Python, PostgreSQL, Prefect, AWS * AI: batch LLM extraction, embeddings, OCR Compensation RAL €35,000 - €45,000 + equity, depending on profile. Hiring Manager Lorenzo Rossetto [email protected]

📌 Junior data engineer - España (Madrid)
🏢 Cato
📍 Madrid