Job Opportunities API

Join Riverside Natural Foods Ltd., a Canadian-based, family-owned, and globally operating business, committed to leaving the world better than we found it. As a B-Corp certified, Triple-Bottom Line company, we proudly manufacture nutritious, 'better-for-you' snacks such as MadeGood and GOOD TO GO. We value teamwork, humility, respect, ownership, adaptability, grit, and fun. We’re on an ambitious mission to double our business by 2027, and we need talented individuals like you to help us reach new heights. At Riverside, you’ll have the opportunity to chart your own path to success while contributing to ours. We believe anything worth doing is worth doing right, and our values will guide us through the rugged terrain – and yes, it will get rough. But that’s what makes the journey worthwhile. So, lace up your boots and let’s tackle the climb together. You can learn more about us at  www.riversidenaturalfoods.com .   Position Summary:   Riverside Natural Foods is investing in modern data and analytics capabilities to support business growth and innovation. As part of this transformation, Databricks is becoming the central platform for all non-SAP data, enabling advanced analytics, reporting, and emerging AI use cases.   The Data Platform Engineer (Databricks) will play a key hands-on role in designing, building, and operating this platform. This role is responsible for delivering end-to-end data solutions—from ingestion and transformation to modelling and consumption—while shaping best practices for scalable, reliable, and high-performing data pipelines.   This is a highly hands-on role ideal for someone who enjoys building, solving complex data challenges, and owning technical delivery within a modern cloud data platform.   Primary Responsibilities:   1.  Databricks Platform Ownership   Design, build, and maintain the Databricks platform as the central hub for non-SAP data   Define and implement best practices for data architecture, pipeline development, and platform usage   Ensure platform scalability, performance, reliability, and cost efficiency   2. Data Engineering & Pipeline Development   Develop and maintain scalable data pipelines using Databricks   Ingest data from a variety of sources, including APIs and external data providers, streaming platforms (e.g., Kafka), Internal non-SAP systems, etc.   Build ETL/ELT workflows to support analytics and reporting needs   Support both batch and streaming data processing patterns   3. Data Modeling & Architecture   Design and implement data models to support analytics and reporting use cases   Define how data is structured, stored, and accessed within Databricks   Enable integration with SAP Datasphere where required, while maintaining flexibility for independent datasets   4. End-to-End Data Delivery   Deliver complete data solutions from ingestion to consumption   Collaborate with analytics and reporting team members to ensure data usability and performance   Support downstream use cases, including dashboards, reporting, and advanced analytics   5. AI & Advanced Analytics Enablement   Enable AI and advanced analytics capabilities within Databricks   Support data preparation for machine learning and AI-driven use cases   Leverage built-in Databricks features to accelerate innovation   6. Engineering Best Practices & Tooling   Use GitHub for version control and collaborative development   Implement CI/CD practices for data pipelines and platform components   Improve automation, testing, and documentation across the platform   7. Vendor & Partner Coll