We are seeking a highly skilled Data Engineer with deep expertise in PySpark and the Cloudera Data Platform (CDP) to join our data engineering team. As a Data Engineer, you will be responsible for designing, developing, and maintaining scalable data pipelines that ensure high data quality and availability across the organization. This role requires a strong background in big data ecosystems, cloud-native tools, and advanced data processing techniques.
The ideal candidate has hands-on experience with data ingestion, transformation, and optimization on the Cloudera Data Platform, along with a proven track record of implementing data engineering best practices. You will work closely with other data engineers to build solutions that drive impactful business insights.
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. - 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.
Education and Experience
- Bachelor’s or Master’s 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.
- 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.
- Strong analytical and problem-solving 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.
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