Cargill is committed to providing food and agricultural solutions to nourish the world in a safe, responsible, and sustainable way. Sitting at the heart of the supply chain, we partner with farmers and customers to source, make and deliver products that are vital for living. Our 155,000 team members innovate with purpose, providing customers with life’s essentials so businesses can grow, communities prosper, and consumers live well. With over 160 years of experience as a family company, we look ahead while remaining true to our values. We put people first. We reach higher. We do the right thing—today and for generations to come.
Job Purpose and Impact
The Senior Data Engineer job designs, builds and maintains complex data systems that enable data analysis and reporting. With minimal supervision, this job ensures that large sets of data are efficiently processed and made accessible for decision making.
Key Accountabilities
- DATA INFRASTRUCTURE: Prepares data infrastructure to support the efficient storage and retrieval of data.
- DATA FORMATS: Examines and resolves appropriate data formats to improve data usability and accessibility across the organization.
- DATA & ANALYTICAL SOLUTIONS: Develops complex data products and solutions using advanced engineering and cloud based technologies, ensuring they are designed and built to be scalable, sustainable and robust.
- DATA PIPELINES: Develops and maintains 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 identify areas for improvement and optimization.
- STAKEHOLDER MANAGEMENT: Collaborates with multi-functional data and advanced analytic teams to gain requirements and ensure that data solutions meet the functional and non-functional needs of various partners.
- DATA FRAMEWORKS: Builds complex prototypes to test new concepts and implements data engineering frameworks and architectures that improve data processing capabilities and support advanced analytics initiatives.
- AUTOMATED DEPLOYMENT PIPELINES: Develops automated deployment pipelines improving efficiency of code deployments with fit for purpose governance.
- DATA MODELING: Performs complex data modeling in accordance to the datastore technology to ensure sustainable performance and accessibility.
Qualifications
Minimum requirement of 4 years of relevant work experience. Typically reflects 5 years or more of relevant experience.
Preferred Qualifications:
- CLOUD ENVIRONMENTS: Experience developing data systems on major cloud platforms (AWS, GCP, Azure).