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# Data Engineer

- **Company:** Ai-native
- **Location:** Barcelona, Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Artificial Intelligence, Airflow, BigQuery, Software as a Service, Cloud Database, Cloud Storage, Information Engineering, Data Governance, Data Infrastructure, Data Warehousing, Python (Programming Language), Standard Sql, SQL Databases, Data Streaming, Workflow Management Systems, Datadog, Google Cloud, Large Language Models, Core Data, Apache Kafka
- **Published:** July 11, 2026
- **Apply:** https://www.jobleads.com/es/job/e6d5d264f9b04e725a12d23647c0759c5

## About the Role

* 5+ years of hands-on data engineering experience, ideally in a startup or scale-up. We'll consider exceptional candidates with less experience but a proven track record of owning data infrastructure end-to-end, not just contributing to it.
 * You've built a data warehouse or data platform from scratch, or led a migration of one at scale.
 * Strong SQL and Python. Solid hands-on experience with BigQuery or similar cloud data warehouses.
 * You're AI-native: you use AI tools daily, stay current with new models and techniques, and apply them to move faster and build better.
 * Product mindset: you understand business context, talk to stakeholders, and care about outcomes not just clean code.
 * Fluent in Spanish and comfortable in English.

You'll stand out if you have experience with

 * SaaS companies with multi-team data needs
 * dbt, ideally a project with 50+ models in production
 * Orchestration tools like Airflow, Prefect, or Dagster
 * Event streaming with Kafka, Pub/Sub, or similar
 * Data quality frameworks and observability tooling
 * Working with AI/LLM traces and usage data

## Description

* Design the full data architecture: from raw ingestion to clean, modeled data ready for AI and analytics
 * Build robust pipelines to ingest data from all our systems: product, CRM, finance, marketing, and AI traces
 * Model core data entities so every team works from the same source of truth
 * Ensure data quality and observability: monitoring, testing, and alerting so we trust what we ship
 * Power our AI initiatives: lay the data foundations that our AI workforce depends on to work reliably at scale
 * Instrument product usage: events, funnels, retention, giving Product the data they need to make good decisions
 * Put the whole company on the same page: build dashboards and datasets that Sales, CX, Finance, and Marketing actually use, Python, SQL, BigQuery, Google Cloud Storage, Google Cloud Platform, PostHog, and several AI providers.


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