We are seeking a highly skilled Data Engineer with deep expertise in PySpark and the Cloudera Data Platform (CDP) to join our data engineering team. As a Data Engineer you will be responsible for designing developing and maintaining scalable data pipelines that ensure high data quality and availability across the organization. This role requires a strong background in big data ecosystems cloud-native tools and advanced data processing techniques.
The ideal candidate has hands-on experience with data ingestion transformation and optimization on the Cloudera Data Platform along with a proven track record of implementing data engineering best practices. You will work closely with other data engineers to build solutions that drive impactful business insights.
Responsibilities
Data Pipeline Development: Design develop and maintain highly scalable and optimized ETL pipelines using PySpark on the Cloudera Data Platform ensuring data integrity and accuracy.
Data Ingestion: Implement and manage data ingestion processes from a variety of sources (e.g. relational databases APIs file systems) to the data lake or data warehouse on CDP.
Data Transformation and Processing: Use PySpark to process cleanse and transform large datasets into meaningful formats that support analytical needs and business requirements.
Performance Optimization: Conduct performance tuning of PySpark code and Cloudera components optimizing resource utilization and reducing runtime of ETL processes.
Data Quality and Validation: Implement data quality checks monitoring and validation routines to ensure data accuracy and reliability throughout the pipeline.
Automation and Orchestration: Automate data workflows using tools like Apache Oozie Airflow or similar orchestration tools within the Cloudera ecosystem.
Monitoring and Maintenance: Monitor pipeline performance troubleshoot issue.
Required Experience:
Senior IC
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