Cloud Data Engineer AI Foundations
Summary
Data engineer at Agworld/Semios designing, building, and maintaining cloud data pipelines and AI-ready datasets for agricultural technology products, working with advanced SQL, Python/Node, and multi-cloud tools like BigQuery, Redshift, Snowflake, GCP, and AWS.
Build scalable cloud data systems at Semios, using advanced SQL and Python to create trusted datasets for agricultural technology products.
As a Data Engineer, you will design, develop, and maintain production data pipelines across multi-cloud environments. You will collaborate with Engineering and Data Science teams while integrating diverse sources, optimizing infrastructure, and creating AI-ready data foundations. Your work will support analytics, machine learning, and data-driven agricultural insights.
Key Responsibilities:
- Architect cloud systems connecting Semios data with consumers
- Build scalable infrastructure for processing and storing data
- Develop models turning raw measurements into trusted datasets
- Optimize pipeline performance, cost, migrations, and refactoring
- Document data structures and Data APIs across products
Requirements:
- Three to five years of relevant Data Engineering experience
- Advanced SQL and production pipeline development skills
- Experience with BigQuery, Redshift, Snowflake, or equivalents
- Cloud experience, particularly GCP and AWS platforms
- Fluency in Python, Node, or another imperative language
Shape Semios data platforms through cloud engineering, advanced SQL, reliable pipelines, and AI-ready dataset development.