Your mission

Bring 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.

Compruebe que cumple con los requisitos de habilidades para este puesto, así como con la experiencia asociada, y luego envíe su CV a continuación.

What you'll 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 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.

Idóneo profile

Python that holds up: typed, tested, and readable six months later.

You've scraped something real: 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 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, Airflow, Dagster) is a plus, not a requirement: you'll learn ours properly.

Exposure to LLM-based extraction is welcome; curiosity about it is mandatory.

What you won't 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 "we've always done it this way" excuses: we're here to disrupt, not to follow old patterns. xqziphu

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

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