Data Engineer

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 doOwn 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 profileReal 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.Experience2-4 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 hereNo 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 CompensationRAL €40,000 - €60,000 + equity, depending on seniority and profile.#J-*****-Ljbffr

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.

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. #J-*****-Ljbffr