2 months ago
Remote, Spain or Madrid, SpainMid Level
Responsibilities
- Design, develop, and maintain ETL and data transformation processes.
- Build, optimize, and support Spark-based data pipelines.
- Develop scalable data processing solutions in a Big Data environment.
- Integrate data from multiple sources while ensuring data quality and consistency.
- Analyze, troubleshoot, and resolve data-related issues.
- Optimize data performance and processing efficiency.
- Collaborate with technical, business, and cross-functional stakeholders to gather and refine requirements.
- Develop Qlik Sense reports and dashboards.
- Participate in Agile ceremonies and continuous improvement initiatives.
- Apply DevOps and CI/CD practices throughout the development lifecycle.
Requirements
- At least 3 years of experience in software development and data engineering.
- Strong knowledge of SQL, including ANSI SQL, Hive SQL, Spark SQL, or equivalent.
- Hands-on experience with Apache Spark.
- Experience with at least one of Scala, Python, or Java.
- Experience with a Spark-based platform such as Azure Databricks, Amazon EMR, Cloudera, Apache Hive, or a similar Big Data environment.
- Experience with Qlik Sense reporting and dashboard development.
- Familiarity with Agile methodologies and DevOps practices.
- Experience with version control and CI/CD tools such as Git, GitHub, SonarQube, or Nexus.
- Strong analytical, problem-solving, collaboration, and requirements analysis skills.
- English proficiency at B2+ level or above.
- Interest or experience in AI-assisted development tools is a plus.
- Knowledge of AI-driven approaches for data engineering, automation, and analytics is a plus.
- Experience optimizing processes through AI technologies is a plus.
Benefits
- Permanent, full-time contract.
- Career development opportunities and training in finance, technology, and data management.
- Work in a challenging, multicultural environment with international projects.
- Exposure to large-scale financial IT systems and cross-country operations.
Categories
Data Engineering
