Data Engineer

• DATA & ANALYTICAL SOLUTIONS: 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.
• DATA PIPELINES: 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.
• DATA SYSTEMS: Reviews existing data systems and architectures to implement the identified areas for improvement and optimization.
• DATA INFRASTRUCTURE: Helps prepare data infrastructure to support the efficient storage and retrieval of data.
• DATA FORMATS: Implements appropriate data formats to improve data usability and accessibility across the organization.
• STAKEHOLDER MANAGEMENT: 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.
• DATA FRAMEWORKS: 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.
• AUTOMATED DEPLOYMENT PIPELINES: Implements automated deployment pipelines to support improving efficiency of code deployments with fit for purpose governance.
• DATA MODELING: Performs moderately complex data modeling aligned with the datastore technology to ensure sustainable performance and accessibility.

• 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.

What We Do

We are a family company providing food ingredients agricultural solutions and industrial products to nourish the world in a safe responsible and sustainable way. We connect farmers with markets so they can prosper. We connect customers with ingredients so they can make meals people love. And we connect families with daily essentials— from eggs to edible oils salt to skincare feed to flooring. By providing customers with products that are vital for living we help businesses grow communities prosper and consumers live well in their daily lives.

Why Work With Us

The decision to join Cargill can open the door to a world of possibility. As part of our Digital Technology & Data team you’ll get to be part of a large and diverse group full of unique perspectives united by a common higher purpose while building a rewarding career full of opportunity growth and the satisfaction of knowing your work matters.

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