On behalf of our client, we are seeking a passionate Data Engineer (with GCP & BigQuery experience) to drive a high-stakes cloud transformation within their "Corporate Stock Valuation" agile team. The core mission of this position is to lead the modernization of their financial data infrastructure, migrating mission-critical processes from legacy systems (Teradata) to Google Cloud (BigQuery). Because the data involved represents 30% of the client's total balance sheet, this role is highly impactful: your solutions will directly guarantee the accuracy, speed, and reliability of their integral financial reporting and decision-making. Our client is looking for a proactive, team-oriented tech professional—fluent in both English and German—who brings a strong agile and DevOps mindset. The ideal candidate has a deep understanding of cloud architecture, data integration, and automation, and thrives on solving complex challenges. Contract: Architect and optimize the transition of our financial engine from legacy Teradata to GCP (BigQuery), leveraging modern cloud capabilities.




Iterate on existing data pipelines and solutions to ensure long-term stability and performance. Evolve Development Practices: Work cross-functionally to solve technical roadblocks and actively improve agile development processes.
A degree in IT, Business Informatics, or equivalent practical experience in software/data engineering roles within agile environments (Scrum/Kanban, DevOps, CI/CD).GCP & Data Expertise: Deep hands-on experience with Google Cloud Platform, particularly BigQuery SQL, Cloud Storage, Dataflow, and Pub/Sub.
Data Migration Knowledge: Solid track record in data integration and migration projects, specifically transitioning systems from Teradata to BigQuery (or similar legacy-to-cloud environments).Infrastructure & Testing: Proficiency with Infrastructure-as-Code (Terraform), version control (Git), and automated testing practices.
Fluency in both English and German is essential for sea

📌 Data Engineer (Madrid)
🏢 Babel Profiles
📍 Madrid