Senior Data Engineer; PySpark

Overview

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.

• 8+ years of experience as a Data Engineer, with a strong focus on PySpark and the Cloudera Data Platform.

• 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 issues, and perform routine maintenance on the Cloudera Data Platform and associated data processes.
• Collaboration:
  
  Work closely with other data engineers, analysts, product managers, and other stakeholders to understand data requirements and support various data-driven initiatives.
• Documentation:
  Maintain thorough documentation of data engineering processes, code, and pipeline configurations.

• PySpark:
  Advanced proficiency in PySpark, including working with RDDs, Data Frames, and optimization techniques.
• Cloudera Data Platform:
  Strong experience with Cloudera Data Platform (CDP) components, including Cloudera Manager, Hive, Impala, HDFS, and HBase.
• Data Warehousing:
  Knowledge of data warehousing concepts, ETL best practices, and experience with SQL-based tools (e.g., Hive, Impala).
• Big Data Technologies:
  Familiarity with Hadoop, Kafka, and other distributed computing tools.
• Orchestration and Scheduling:
  Experience with Apache Oozie, Airflow, or similar orchestration frameworks.
• Scripting and Automation:
  Strong scripting skills in Linux.