Experteer Overview

Todos los posibles candidatos deben leer con atención los siguientes detalles de este trabajo antes de presentar una candidatura.

In this role you design, build and optimize scalable data solutions that power analytics, ML, reporting, and critical decision-making. You will work with large data environments to create robust pipelines and distributed processing using Spark-based technologies. You’ll operate in cloud environments to process, transform and expose data for Analytics, Data Science, and business teams. This position offers a chance to shape data platforms and contribute to a leading digital banking ecosystem.

Compensaciones / Incentivos

- Design, develop and optimize large-scale data pipelines using Apache Spark (Scala/Spark).
- Build batch and near-real-time data processing for high-volume environments.
- Create reliable datasets consumed by analytics, data science, ML, and business teams.
- Work with cloud data platforms (ideally AWS) to process, transform and store data.
- Implement data quality, validation, monitoring and documentation across pipelines.
- Collaborate with Data Engineering, Data Science, ML, Architecture and business teams to deliver practical data solutions.
- Contribute to engineering best practices around code quality, testing, CI/CD and automation.

Responsabilidades





- 5+ years in Data Engineering or similar roles.
- Experience building production-grade data pipelines in large-scale environments.
- Hands-on Spark experience in real projects, preferably Scala.
- Experience with cloud data platforms.
- Experience in banking, fintech or regulated environments (preferred).
- Fluent Spanish; professional English.
- Strong Python or Scala coding skills for data engineering tasks.
- Solid SQL knowledge and experience with relational/analytic databases.
- Experience designing and optimizing ETL/ELT processes.
- Experience with AWS or equivalent cloud ecosystems (S3, Glue, Athena, EMR, Redshift, IAM, Lake Formation).
- Experience with Git and collaborative software development practices.
- Understanding of data quality, validation, monitoring and performance optimization.
- Experience with CI/CD or DevOps tools (Jenkins, Sonar, Nexus, Jira, Splunk).
- Scala experience (preferred).
- Experience with Apache Flink, Spark Streaming or near-real-time applications (preferred).
- Experience with Iceberg, Delta Lake or modern lakehouse formats (preferred). xugodme
- Experience with Airflow or workflow orchestration tools (preferred).

Requisitos principales

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📌 Senior Data Engineer - ODS (Madrid)
🏢 Grupo Santander
📍 Madrid