Data Engineer - Pyspark

Virtusa; Dubai; On-site; AED 120,000 - 180,000; Full time

Job summary

An established industry player is seeking a skilled Data Engineer to join their dynamic team. In this role, you will design and develop highly scalable ETL pipelines using PySpark on the Cloudera Data Platform, ensuring data integrity and accuracy. You will implement data ingestion processes, transform large datasets, and optimize performance while collaborating with various stakeholders. This is an exciting opportunity to work in a collaborative environment where your contributions will directly impact data-driven initiatives. If you have a passion for big data technologies and a commitment to quality, this role is perfect for you.

Qualifications

• 3 years of experience as a Data Engineer focusing on PySpark and Cloudera.
• Bachelor's or Master's degree in Computer Science or related field.

Responsibilities

• Design and maintain ETL pipelines using PySpark on the Cloudera Data Platform.
• Implement data ingestion processes from various sources to the data lake.

Skills

PySpark

Data Engineering

Analytical Skills

Problem-solving

Communication Skills

Education

Bachelor's degree in Computer Science

Master's degree in Computer Science

Tools

Cloudera Data Platform

Apache Oozie

Apache Airflow

SQL

Hadoop

Kafka

Job description

Pyspark Job Description:

Responsibilities

1. 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.
2. 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.
3. Data Transformation and Processing: Use PySpark to process, cleanse, and transform large datasets into meaningful formats that support analytical needs and business requirements.
4. Performance Optimization: Conduct performance tuning of PySpark code and Cloudera components, optimizing resource utilization and reducing runtime of ETL processes.
5. Data Quality and Validation: Implement data quality checks, monitoring, and validation routines to ensure data accuracy and reliability throughout the pipeline.
6. Automation and Orchestration: Automate data workflows using tools like Apache Oozie, Airflow, or similar orchestration tools within the Cloudera ecosystem.
7. Monitoring and Maintenance: Monitor pipeline performance, troubleshoot issues, and perform routine maintenance on the Cloudera Data Platform and associated data processes.
8. Collaboration: Work closely with other data engineers, analysts, product managers, and other stakeholders to understand data requirements and support various data-driven initiatives.
9. Documentation: Maintain thorough documentation of data engineering processes, code, and pipeline configurations.

Qualifications

Education and Experience

1. Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related field.
2. 3 years of experience as a Data Engineer with a strong focus on PySpark and the Cloudera Data Platform.

Technical Skills

1. PySpark: Advanced proficiency in PySpark, including working with RDDs, DataFrames, and optimization techniques.
2. Cloudera Data Platform: Strong experience with Cloudera Data Platform (CDP) components including Cloudera Manager, Hive, Impala, HDFS, and HBase.
3. Data Warehousing: Knowledge of data warehousing concepts, ETL best practices, and experience with SQL-based tools (Hive, Impala).
4. Big Data Technologies: Familiarity with Hadoop, Kafka, and other distributed computing tools.
5. Orchestration and Scheduling: Experience with Apache Oozie, Airflow, or similar orchestration frameworks.
6. Scripting and Automation: Strong scripting skills in Linux.

Soft Skills

1. Strong analytical and problem-solving skills.
2. Excellent verbal and written communication abilities.
3. Ability to work independently and collaboratively in a team environment.
4. Attention to detail and commitment to data quality.