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.

📌 Cloud Data Engineer AI Foundations (Vancouver)
🏢 Agworld
📍 Vancouver