We are looking for a skilled Data Engineer to join a high-performing data engineering environment and contribute to the design, development, integration and optimization of enterprise-scale data solutions.

The ideal candidate will have strong hands-on experience across the Cloudera Data Platform (CDP) and the broader Hadoop ecosystem, with proven expertise in building robust ETL pipelines, processing large datasets and supporting data analytics initiatives.

Key Responsibilities

• Design, develop and maintain scalable Big Data and ETL data pipelines.
  
• Work extensively with the Cloudera Data Platform (CDP) and Hadoop ecosystem.
  
• Develop and optimize data processing solutions using Apache Spark and PySpark.
  
• Build and manage data ingestion pipelines using Apache NiFi and Sqoop.
  
• Work with HDFS, Hive and Impala for large-scale data storage, processing and querying.
  
• Develop complex and optimized SQL queries for data extraction, transformation and analysis.
  
• Develop data engineering solutions using Python and Shell scripting.
  
• Integrate and process data from enterprise data sources, including Oracle.
  
• Develop, maintain and optimize ETL processes to support business and analytical requirements.
  
• Monitor data pipelines and scheduled workloads using Control-M.
  
• Perform troubleshooting, performance tuning and root-cause analysis across data processing environments.
  
• Work within Linux/Unix environments to administer, troubleshoot and automate data engineering processes.
  
• Support data quality, data integrity and data availability across enterprise data platforms.
  
• Collaborate with Data Analysts, Developers, Architects, Business Analysts and other technology teams.
  
• Contribute to the continuous improvement of data engineering standards, processes and platforms.
  

Requirements

• Design, develop and maintain scalable Big Data and ETL data pipelines.
  
• Work extensively with the Cloudera Data Platform (CDP) and Hadoop ecosystem.
  
• Develop and optimize data processing solutions using Apache Spark and PySpark.
  
• Build and manage data ingestion pipelines using Apache NiFi and Sqoop.
  
• Work with HDFS, Hive and Impala for large-scale data storage, processing and querying.
  
• Develop complex and optimized SQL queries for data extraction, transformation and analysis.
  
• Develop data engineering solutions using Python and Shell scripting.
  
• Integrate and process data from enterprise data sources, including Oracle.
  
• Develop, maintain and optimize ETL processes to support business and analytical requirements.
  
• Monitor data pipelines and scheduled workloads using Control-M.
  
• Perform troubleshooting, performance tuning and root-cause analysis across data processing environments.
  
• Work within Linux/Unix environments to administer, troubleshoot and automate data engineering processes.
  
• Support data quality, data integrity and data availability across enterprise data platforms.
  
• Collaborate with Data Analysts, Developers, Architects, Business Analysts and other technology teams.
  
• Contribute to the continuous improvement of data engineering standards, processes and platforms.