Data Engineer PySpark
Posted on :
15-03-2025
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1 Vacancy
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Job Description
Pyspark JD:ResponsibilitiesData 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 (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 datadriven initiatives.Documentation: Maintain thorough documentation of data engineering processes code and pipeline configurations.QualificationsEducation and ExperienceBachelors or Masters degree in Computer Science Data Engineering Information Systems or a related field.3 years of experience as a Data Engineer with a strong focus on PySpark and the Cloudera Data Platform.Technical SkillsPySpark: Advanced proficiency in PySpark including working with RDDs DataFrames 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 SQLbased tools (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.Soft SkillsStrong analytical and problemsolving skills.Excellent verbal and written communication abilities.Ability to work independently and collaboratively in a team environment.Attention to detail and commitment to data quality.
Employment Type
Full-Time
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