Sr. Data Engineer

Job Purpose and Impact

• The Senior Professional, Data Engineering 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. Within Food Data Engineering Americas, this role builds and operates data products on Cargill’s Minerva platform, supporting the ongoing migration off CDP and enabling scalable, governed data solutions for Supply Chain, Procurement & Manufacturing, Commercial Excellence, and LATAM domains.

  
  
  

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 it 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 with the datastore technology to ensure sustainable performance and accessibility.
• Minerva Data Product Delivery: Builds and maintains data products within the Minerva Engineering Framework (MEF), supporting migration of workloads and historical data from CDP to Minerva’s Lakehouse and Compute account architecture.
• Migration Validation: Validates migrated data products using platform reconciliation tooling, ensuring row counts, schema, and aggregate accuracy between legacy (CDP/Impala) and Minerva (AWS Athena/Lakehouse) sources.

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). Hands-on AWS experience strongly preferred given Minerva’s AWS-native architecture.
• Data Architecture: Hands-on experience building modern data architectures, including data lakes, data lakehouses, and data hubs, along with related capabilities such as ingestion, governance, modeling, and observability.
• Data Ingestion: Demonstrated proficiency in data collection, ingestion tools (Kafka, AWS Glue), and storage formats (Iceberg, Parquet).
• Data Streaming: Experience developing data pipelines with streaming architectures and tools (Confluent Kafka, Apache Flink).
• Data Modeling: Expertise in data transformation and modeling using SQL-based frameworks and orchestration tools (dbt, AWS Glue, Airflow/Astronomer). Deep experience with modeling concepts like SCD and sc