Technology Leader, Data Engineering

Enterprise Products Partners
Published
November 15, 2021
Location
Houston, TX
Category
Job Type

Description

Enterprise Products Partners L.P. is one of the largest publicly traded partnerships and a leading North American provider of midstream energy services to producers and consumers of natural gas, NGLs, crude oil, refined products and petrochemicals. Our services include: natural gas gathering, treating, processing, transportation and storage; NGL transportation, fractionation, storage and import and export terminals; crude oil gathering, transportation, storage and terminals; petrochemical and refined products transportation, storage and terminals; and a marine transportation business that operates primarily on the United States inland and Intracoastal Waterway systems. The partnership’s assets include approximately 50,000 miles of pipelines; 260 million barrels of storage capacity for NGLs, crude oil, refined products and petrochemicals; and 14 billion cubic feet of natural gas storage capacity.

We are currently seeking an experienced Data Engineer to join the Big Data and Advanced Analytics department.  As part of the Data Engineering team, the Data Engineer will work closely with Business domain experts and Data Scientists to solve real-world oil and gas midstream problems with advanced analytics, machine learning, and artificial intelligence.  This individual will provide analytical and technical leadership to the team to advance the data engineering practice within the organization.

Responsibilities include:

  • Work directly with Business domain experts and Data Scientists to understand the business objective and develop high quality, scalable, analytical systems  
  • Research new technologies and create architectural strategies for data acquisition, data processing, and data storage which meet defined business objectives
  • Architect, design, and implement data frameworks which streamlines the development and delivery of data products
  • Review existing data architectures and engineering practices to determine overall effectiveness and develop strategies for improving the data engineering discipline
  • Provide mentorship and technical guidance to data engineers and data scientists
  • Perform exploratory data analysis and present findings and recommendations
  • Implement data quality checks to ensure data accuracy, consistency, and reliability
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