Senior Data Engineer; PySpark

Position: Senior Data Engineer (PySpark)

Qualifications

• 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.

Pyspark

Job Description

• 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.

Technical Skills

• 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.

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