Pyspark Data Engineer

VirtusaPolaris - Virtusa Corporation; Dubai; On-site; AED 120,000 - 200,000; Full time

Job summary

A leading technology company in Dubai seeks an experienced Data Engineer to design and develop scalable ETL pipelines using PySpark on the Cloudera Data Platform. The ideal candidate has 8+ years of experience, advanced proficiency in data ingestion and transformation, and strong skills in performance optimization and data quality. Knowledge of orchestration tools like Apache Oozie and Airflow, as well as scripting skills in Linux, are also important. This role offers a collaborative environment with opportunities to work on diverse data-driven initiatives.

Qualifications

• 8+ years of experience as a Data Engineer, focusing on PySpark and Cloudera Data Platform.
• Advanced proficiency in working with RDDs and DataFrames in PySpark.
• Strong experience with Cloudera components including Cloudera Manager, Hive, and Impala.

Responsibilities

• Design and develop ETL pipelines using PySpark on Cloudera Data Platform.
• Implement and manage data ingestion from various sources to the data lake.
• Conduct performance tuning of PySpark code and optimize Cloudera components.

Skills

PySpark

Data Engineering

Cloudera Data Platform

SQL

Data Warehousing

Education

Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field

Tools

Apache Oozie

Airflow

Job description

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.

Qualifications

Education and Experience

• 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.

Technical Skills

• PySpark: Advanced proficiency in PySpark, including working with RDDs, DataFrames, and optimization techniques.
• Cloudera Data Platform: Strong experience with 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.

Sof t Skills

• 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.