pyspark data engineer

Job Description

Role & responsibilities Key Skill: Pyspark, Cloudera Data Platfrorm, Big data Hadoop, Hive, Kafka Responsibilities 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. Technical Skills 3+ years of experience as a Data Engineer, with a strong focus on PySpark and the Cloudera Data Platform PySpark: 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 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