Job location
Chesterfield, United States of America
Tech stack
Airflow
Apache HTTP Server
Azure
Information Engineering
ETL
Linux
Hadoop
Python
Cloudera
Spark
Performance Monitor
Software Version Control
Data Pipelines
Job description
loads, data quality issues, and production support issues, monitor ETL workflows and troubleshoot issues to ensure smooth production operations and implement proactive monitoring, alerting, and reporting solutions to improve pipeline reliability. This individual will be responsible for supporting and rebuilding legacy ETL jobs (currently not using ACID transactions) with modern solutions using Apache Spark and Apache Iceberg to support ACID transactions. They will be overseeing design, and implement workflows for automating data pipelines using Apache Airflow as well as establishing and enforcing best practices for ETL code development, version control, and deployment using Azure DevOps.
Requirements
5+ years of Data Engineering experience
Experience with Apache Spark, Airflow or Iceberg
Python programming experience
Production Support experience
Linux experience
Strong communication skills Experience with Precisely Connect
Experience with Hadoop
Experience with Cloudera
About the company
Insight Global is looking to add a 2 Data Engineers to our client's team who are headquartered in the St. Louis area but can sit remotely. They will be responsible for moving applications from one environment to another in addition to ensuring every component has been upgraded and will also be responsible for modernizing the framework as well. They will need someone to come in and support development efforts across the enterprise and will interact with multiple different application owners & software vendors so communication is very important. They will be using Apache Spark, Apache Iceberg, and Apache Airflow for ETL pipelines. This person will transform and integrate EBCDIC Mainframe data into Hive and Impala tables using Precisely Connect for Big Data, optimize data transformation processes for performance, scalability, and reliability, and ensure data consistency, accuracy, and quality across the ETL pipelines. This person will function as the point of escalation for ETL