You own concrete pieces of the data pipeline end to end — not tickets handed to you, but the sources, jobs and tables behind them. Work on merge, dedup and reconciliation — the same tender arrives three times, in three shapes, and only one version can reach the customer.

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.

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.

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

Data & Infra: Python, PostgreSQL, Prefect, K3s/ArgoCD, AWS

📌 Senior Big Data Engineer - Fully Remote (Barcelona)
🏢 Cato
📍 Barcelona