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Cargill · 9 hours ago

Data Engineer

Atlanta, GA

Full-time

Hybrid

Entry, Mid Level

2+ years exp

Cargill is committed to providing food and agricultural solutions to nourish the world in a safe, responsible, and sustainable way. The Data Engineer role involves designing, building, and maintaining moderately complex data systems to enable data analysis and reporting, collaborating to ensure efficient data processing for decision-making.

AgricultureFood and BeverageFood ProcessingManufacturingMeat and Poultry

Comp. & Benefits

No H1B

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

Qualification

Cloud platformsData architectureData ingestion toolsData modelingProgrammingData streamingData transformationDevOpsData governanceStakeholder managementAutomated deployment pipelinesData frameworksData infrastructureData formats

Required

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

Preferred

CLOUD ENVIRONMENTS: Familiarity with major cloud platforms (AWS, GCP, Azure)

DATA ARCHITECTURE: Experience with modern data architectures, including data lakes, data lakehouses, and data hubs, along with related capabilities such as ingestion, governance, modeling, and observability

DATA INGESTION: Proficiency in data collection, ingestion tools (Kafka, AWS Glue), and storage formats (Iceberg, Parquet)

DATA STREAMING: Knowledge of streaming architectures and tools (Kafka, Flink)

DATA MODELING: 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

DATA TRANSFORMATION: Familiarity with using Spark for data transformation, including streaming, performance tuning, and debugging with Spark UI

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

DEVOPS: Demonstrated experience in DevOps practices, including code management, CI/CD, and deployment strategies

DATA GOVERNANCE: Understanding of data governance principles, including data quality, privacy, and security considerations for data product development and consumption