Data Engineer; PySpark

Position: Data Engineer (PySpark)
Location: Bengaluru

Role:
Data Engineer
Key Skill:  Pyspark, Cloudera Data Platform, 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, 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.