As a Pyspark Data Engineer, you will be responsible for designing, developing, and maintaining highly scalable and optimized ETL pipelines using PySpark on the Cloudera Data Platform, ensuring data integrity and accuracy.
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Your main tasks will include:
- Data Pipeline Development: Create and manage ETL pipelines to support analytical needs and business requirements.
- Data Ingestion: Implement data ingestion processes from various sources (e.g., relational databases, APIs) to the data lake or data warehouse.
- Data Transformation and Processing: Process and transform large datasets using PySpark.
- Performance Optimization: Conduct performance tuning of PySpark code and Cloudera components to optimize resource utilization.
- Data Quality and Validation: Ensure data accuracy and reliability through monitoring and validation routines.
- Automation and Orchestration: Automate workflows using tools like Apache Oozie or Airflow.
- Monitoring and Maintenance: Monitor pipeline performance and perform routine maintenance on the Cloudera Data Platform.
- Collaboration: Work closely with data engineers, analysts, and stakeholders to support data-driven initiatives.
- Documentation: Maintain thorough documentation of data engineering processes.
Technical Skills:
- Advanced proficiency in PySpark, with a solid understanding of RDDs, DataFrames, and optimization techniques.
- Strong experience with Cloudera Data Platform components, including Cloudera Manager, Hive, Impala, HDFS, and HBase.
- Knowledge of data warehousing concepts and ETL best practices, with experience in SQL-based tools.
- Familiarity with big data technologies like Hadoop and Kafka.
- Experience with orchestration frameworks such as Apache Oozie or Airflow.
- Strong scripting skills in Linux.
Desired Skills and Experience:
- Experience with AWS Native Data Services (optional).
Work Conditions:
- Hybrid work environment.
- Full-time position.
Language Requirements:
- (No specific language requirements mentioned in the job posting.)
