On behalf of our client, we are seeking a passionate Data Engineer (with GCP & Big Query experience) to drive a high-stakes cloud transformation within their "Corporate Stock Valuation" agile team.

¿Tiene las cualificaciones y habilidades adecuadas para este trabajo? Descúbralo a continuación y pulse en "solicitar" para ser considerado.

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 (Big Query).

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 global 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 Dev Ops mindset.

The idóneo candidate has a deep understanding of cloud architecture, data integration, and automation, and thrives on solving complex challenges.





More than just a developer, the client needs a strategic partner who can collaborate seamlessly with financial stakeholders, bridging the gap between business needs and resilient, scalable technical solutions.

Key languages: Full Professional Level of English Full Professional Level of German Main responsibilities: Modernize Core Architectures: Architect and optimize the transition of our financial engine from legacy Teradata to GCP (Big Query), leveraging modern cloud capabilities.

Develop Critical Business Simulations: Design and execute complex financial simulations that empower business leadership during key operational events.

Drive Continuous Enhancement: Iterate on existing data pipelines and solutions to ensure long-term stability and performance.

Empower Your Peers: Elevate team capabilities through peer code reviews, technical guidance, and knowledge sharing.

Translate Finance into Cod

📌 Software engineer (Madrid)
🏢 Babel Profiles
📍 Madrid