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Senior Data Engineer âÃÂàBig Data & Cloudera at Sabenza IT
Sabenza IT
September 29, 2026
Full-time
On-site
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
7 - 8 years of solid hands-on experience as a platform and data engineer (intermediate to senior level).
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.
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
7 - 8 years of solid hands-on experience as a platform and data engineer (intermediate to senior level).
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.