Data engineer

Cato; Barcelona; Presencial; EUR 40.000 - 60.000; Jornada completa

Descripción de la vacante

Cato is hiring a data engineer to build and run the pipelines that move tenders from source portals to customers. You own ingestion, merge, enrichment, and delivery end to end, with real ownership over data sources, jobs and tables.

You will work on scrapers for portals in Spain and Italy, write robust orchestrations (Prefect, Airflow, Dagster, Argo), perform AI enrichment with LLM extraction, and ensure production SQL performance.

Formación

• 2–4 years building data pipelines in production.
• Hands-on with PostgreSQL beyond writing queries—you've had to make one fast.
• Exposure to LLM-based extraction is welcome; curiosity about it is mandatory.

Responsabilidades

• Own scrapers and ingestion for a set of national portals across Spain and Italy.
• Write and maintain orchestrator flows: retries, backfills, alerting, and validation.
• Work on merge, dedup and reconciliation to deliver a single correct tender.
• Ship AI enrichment steps: batch LLM extraction, embeddings, OCR on attachments.
• Write production SQL with query plans, indexes, JSONB, partitioning, and migrations.
• Guard data quality with tests and checks that fail loudly.

Conocimientos

SQL mastery

Python production

Data pipelines

Automation mindset

Troubleshooting

Herramientas

PostgreSQL

Prefect

Airflow

Dagster

ArgoWorkflows

JSONB

Descripción del empleo

Build and run the pipelines that carry a tender from the source portal to the customer's screen: ingestion, merge, enrichment, delivery. You own concrete pieces of the data pipeline end to end — not tickets handed to you, but the sources, jobs and tables behind them.

What you'll actually do

• Own scrapers and ingestion for a set of national portals across Spain and Italy, where reading the source in its original language is part of the job.
• Write and maintain orchestrator flows: retries, backfills, alerting, and a clear answer to "Did today's run actually land?"
• Work on merge, dedup and reconciliation — 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.
• Write SQL that survives production: query plans, indexes, JSONB, partitioning, CONCURRENTLY migrations.
• Guard data quality with tests and checks that fail loudly before a customer finds the gap.

Ideal profile

• Real SQL: you can read an EXPLAIN and say why the plan is bad, not just that it is slow.
• Python you'd put in production: typed, tested, and readable six months later.
• Pipelines you've actually operated: with an orchestrator (Prefect, Airflow, Dagster, ArgoWorkflows) and the 3 a.m. failures that come with them.
• 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.

Experience

• 2–4 years building data pipelines in production.
• Hands-on with PostgreSQL beyond writing queries — you've had to make one fast.
• 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.
• AI: batch LLM extraction, embeddings, OCR

Compensation

RAL €40,000 – €60,000 + equity, depending on seniority and profile.