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Job Purpose and Impact

The Sr. Data Platform Engineer will be focused on Cargill's Data Engineering Frameworks. They will take the lead in building the data and analytics platform for modern business applications in the company. In this role, you will apply your in-depth knowledge of modern infrastructure and cloud software engineering practices to build, secure and maintain the core capabilities used by our data and application teams to drive business value. You will also coach and mentor junior engineers to deliver highly scalable and resilient systems using infrastructure as code across our data centers and cloud environments.

Key Accountabilities

• Design, build, and maintain scalable platforms and infrastructure using deep expertise in software development and Infrastructure as Code (IaC).
• Lead end-to-end development of automated infrastructure components—design, test, deploy, support, and enhance.
• Drive complex initiatives and ensure timely delivery of large-scale tasks and stories.
• Contribute to the engineering community by sharing modern practices, reusable frameworks, and promoting code quality standards.
• Prototype innovative solutions and advocate for adoption of emerging technologies.
• Independently resolve complex technical issues with minimal supervision, escalating only when necessary.
• Collaborate cross-functionally with product managers, designers, and engineers to gather requirements and deliver business-aligned solutions.
• Provide technical support and troubleshoot critical application issues to minimize downtime and ensure rapid resolution.

Qualifications

Minimum Qualifications

• Bachelor’s degree or equivalent experience
• 4+ years in relevant roles

Preferred Qualifications

• Built internal tools/frameworks for team enablement
• Designed scalable batch data platforms (real-time a plus)
• CI/CD and cloud automation (Jenkins, GitHub Actions, etc.)
• Infra as code (Terraform, CloudFormation)
• Cloud/serverless experience (AWS preferred)
• Skilled in SQL, Python, Java
• Experience with dbt, data modeling, and transformation tools
• Orchestration tools (Airflow, Glue, Dagster, etc.)
• Familiar with Docker, Kubernetes, Kafka, Flink
• Knowledge of modern data stacks (Snowflake, Databricks)
• Observability tools (Datadog, CloudWatch, Prometheus)
• Open table formats and Lakehouse components (Iceberg, Glue, Lake Formation)
• Strong documentation, mentoring, and collaboration skills
• Agile mindset and critical thinking