Data Engineer

cargillAtlanta, GA

About The Position

The Professional, Data Engineering job designs, builds and maintains moderately complex data systems that enable data analysis and reporting. With limited supervision, this job collaborates to ensure that large sets of data are efficiently processed and made accessible for decision making.

Requirements

  • Minimum requirement of 2 years of relevant work experience. Typically reflects 3 years or more of relevant experience.

Nice To Haves

  • Professional experience as Software Engineer
  • Proficient with programming in Python, SQL, or similar languages. Expert-level proficiency in SQL for data manipulation and optimization.
  • Snowflake
  • Experience working with SAP or similar enterprise software platforms.
  • Demonstrated experience in DevOps practices, including code management, CI/CD, and deployment strategies.

Responsibilities

  • Develops moderately complex data products and solutions using advanced data engineering and cloud based technologies, ensuring they are designed and built to be scalable, sustainable and robust.
  • Maintains and supports the development of streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.
  • Reviews existing data systems and architectures to implement the identified areas for improvement and optimization.
  • Helps prepare data infrastructure to support the efficient storage and retrieval of data.
  • Implements appropriate data formats to improve data usability and accessibility across the organization.
  • Partners with multi-functional data and advanced analytic teams to collect requirements and ensure that data solutions meet the functional and non-functional needs of various partners.
  • Builds moderately complex prototypes to test new concepts and implements data engineering frameworks and architectures to support the improvement of data processing capabilities and advanced analytics initiatives.
  • Implements automated deployment pipelines to support improving efficiency of code deployments with fit for purpose governance.
  • Performs moderately complex data modeling aligned with the datastore technology to ensure sustainable performance and accessibility.
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