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# Mid Developer Big Data | 100% Remote

- **Company:** UST
- **Location:** Madrid, Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Agile Methodology, Big Data, Computer Programming, Continuous Integration, Data Integration, Extract Transform Load (ETL), Data Transformation, Data Systems, DevOps, Github, Apache Hive, Python (Programming Language), Cisco Nexus Switches, Standard Sql, Cloudera, SONAR (Symantec), SQL Databases, Qliksense, Data Processing, Apache Spark, Git, Ansi Sql, Data Management, Amazon Elastic Mapreduce (EMR), Databricks
- **Published:** July 11, 2026
- **Apply:** https://es.indeed.com/viewjob?jk=d90a94d4dd5f53df

## About the Role

* 3+ years of experience in data and programming
 * Good SQL skills ( ansi SQL, Hive SQL, Spark sql, - SQL in general)
 * Good SPARK basis + programming language that could be: scala , python or java
 * Azure Databricks / Amazon EMR / Hive / Cloudera (or any spark env)
 * Experience in Reporting with Qlik sense.
 * Experience working in agile continuous integration/DevOps paradigm and tool set (Git, GitHub, Sonar, Nexus)
 * Ability to work in a team.
 * Ability to analyze requirements and cooperate with team members to improve it.
 * English (at least B2+)

## Description

We are looking for a Mid-Level Developer Big Data who will be responsible for the development, maintenance, and optimization of data processing solutions within the Client Contribution platform., * Designing and developing ETL/data transformation processes, implementing and maintaining Spark-based pipelines, supporting data integration initiatives, analyzing and troubleshooting data-related issues, and ensuring data quality and performance across the platform.

 * The role requires hands-on experience with Spark, SQL, ( with Scala Python or Java), Databricks or similar Big Data environments, and DevOps practices.
 * The candidate should be comfortable working in Agile teams, collaborating with functional and technical stakeholders, analyzing requirements, and contributing proactively to the continuous improvement of data solutions.


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