15 set - Bardi
Cato
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.
Possiede le qualifiche e le competenze giuste per questo lavoro?
Lo scopra qui sotto e invii la sua candidatura per essere preso/a in considerazione.
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.
Ideal profile
Python 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 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.
xlwpduy
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
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