Data Engineer

Cargill•Atlanta, GA

18d

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.
Proficient with programming in Python, Java, Scala, or similar languages.
Expert-level proficiency in SQL for data manipulation and optimization.

Nice To Haves

Familiarity with major cloud platforms (AWS, GCP, Azure).
Experience with modern data architectures, including data lakes, data lakehouses, and data hubs, along with related capabilities such as ingestion, governance, modeling, and observability.
Proficiency in data collection, ingestion tools (Kafka, AWS Glue), and storage formats (Iceberg, Parquet).
Knowledge of streaming architectures and tools (Kafka, Flink).
Strong background in data transformation and modeling using SQL-based frameworks and orchestration tools (dbt, AWS Glue, Airflow).
Experience with modeling concepts like SCD and schema evolution.
Familiarity with using Spark for data transformation, including streaming, performance tuning, and debugging with Spark UI.
Demonstrated experience in DevOps practices, including code management, CI/CD, and deployment strategies.
Understanding of data governance principles, including data quality, privacy, and security considerations for data product development and consumption.

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.