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# Data Engineer Python, PySpark, and SQL

- **Company:** Capgemini
- **Location:** London, UK (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Mining, Data Warehousing, DevOps, Distributed Computing Environment, Revision Control Systems, Python (Programming Language), Performance Tuning, Query Optimization, Standard Sql, SQL Databases, Enterprise Data Management, Data Processing, Pyspark, Data Analytics, Data Pipelines
- **Published:** September 28, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=9dffac58b0bcd572

## About the Role

We are seeking an experienced Data Engineer with strong expertise in Python, PySpark, and SQL to design, develop, and optimize large-scale data processing solutions. The ideal candidate will have hands-on experience building data pipelines, working with big data technologies, and exposure to Scala in enterprise data environments., * 7-9 years of experience in Data Engineering or related roles.

 * Strong hands-on experience in Python development.
 * Proven experience with PySpark for large-scale data processing.
 * Strong knowledge of SQL, including query optimization and performance tuning.
 * Exposure to or working knowledge of Scala.
 * Experience working with cloud-based or enterprise data platforms.
 * Strong analytical, problem-solving, and communication skills.

Preferred Skills

 * Experience with data warehousing and ETL frameworks.
 * Exposure to cloud platforms such as Azure, AWS, or GCP.
 * Familiarity with DevOps, CI/CD, and version control tools.

## Description

* Design, develop, and maintain scalable data pipelines and ETL processes.
 * Develop and optimize data processing solutions using Python and PySpark.
 * Write complex and high-performing SQL queries for data extraction, transformation, and analysis.
 * Work with large datasets and distributed computing frameworks to support data engineering initiatives.
 * Collaborate with architects, analysts, and business stakeholders to deliver data-driven solutions.
 * Troubleshoot and resolve performance, scalability, and data quality issues.
 * Contribute to data platform modernization and best practices.


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