Who you are

- 3–5 years of relevant professional experience in Data Engineering, preferably working with cloud-based data platforms and production data pipelines

- Advanced skills in SQL; how to write elegant queries that minimize data processing, scanning, and latency

- Experience with at least one Data Warehouse (BigQuery, Redshift, Snowflake or an on-prem equivalent) and with Postgres or another relational database

- Excellent verbal and written communication skills: a talent for distilling complex ideas for different audiences

- In-depth experience building and operating production data pipelines at scale, with a track record of reliable collection, storage and access

- Hands-on experience with provisioning and developing on cloud platforms, particularly GCP (BigQuery, Cloud SQL, Cloud Run) and AWS

- Proven experience with workflow and scheduling tools like Prefect, Dagster or similar
- The ability to thrive both autonomously and in a team environment

- Excellent troubleshooting skills to rapidly identify and resolve issues

- Experience with dbt (or a similar SQL-based transformation tool) in production

- Experience integrating AI assistants into your development workflow, and a thoughtful view on where they speed things up and where they need close supervision

- Fluency in Python, Node or other imperative language

- Real-world experience with containers (Docker) & container management systems (Kubernetes)

- Experience with IoT data: ingesting and processing time-series sensor data from field devices in the cloud

- Experience building LLM-based agents to aid development and deployment, and AI agents for customer-facing data analytics

- Advanced education in Big Data, whether from academia, or certifications

What the job involves

- As a Data Engineer at Semios, you will design, build, and maintain scalable,



reliable cloud-based data systems that transform raw data into trusted, analysis-ready datasets. You will work closely with our Engineering and Data Science teams to optimize data pipelines, integrate diverse data sources, and build AI-ready data foundations that power creative products and data-driven insights for agriculture

- Help architect and build cloud-based systems to manage and improve the interface between Semios data and its consumers

- Design, develop and maintain scalable infrastructure to process and store data, integrate data­-driven models and automate manual processes

- Build and maintain models that turn raw measurements into trusted, analysis-ready datasets in a multi-cloud environment

- Design and build reliable, monitorable and fault-tolerant data systems and data processes

- Create data tools for analytics and data science team members that assist them in building and optimizing our product into an innovative industry leader

- Model large, complex datasets that meet business and product requirements

- Optimize pipeline performance and cost (partitioning, clustering, incremental models), and lead refactoring and migrations

- Make our data AI-ready: well-modelled, documented and governed datasets that power LLM-based agents, analytics assistants and ML models

- Build and maintain ingestion pipelines from third-party and partner APIs

- Write clear documentation of data structures and Data APIs across products

Benefits

- Sleep better knowing you're making the world a better place through more sustainable food production

- Opportunity to contribute and make an impact by working on meaningful projects

- Work with a team that values fun, laughter, and each other in collaborative and casual work environment

- Multiple office locations, convenient via transit and bike paths

- Flexible hybrid working environment and hours

📌 Data Engineer (Vancouver)
🏢 Semios
📍 Vancouver