Data Pipeline Development: Design, develop and maintain highly scalable and optimized ETL pipelines using PySpark on the Cloudera Data Platform ensuring data integrity.
Ingestion: Implement and manage data ingestion processes from a variety of sources (relational databases, APIs, file systems) to the data lake or data warehouse.
Transformation and Processing: Use PySpark to process, cleanse and transform large datasets into meaningful formats that support analytical needs and business.
Optimization: Conduct performance tuning of PySpark code and Cloudera components, optimizing resource utilization and reducing runtime of ETL.
Quality and Validation: Implement data quality checks, monitoring and validation routines to ensure data accuracy and reliability throughout.
Orchestration: Automate data workflows using tools like Apache Oozie, Airflow or similar orchestration tools within the Cloudera environment.