Senior Data Platform Engineer

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 2030, 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 [](https://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 Collaboration** Work with external partners and contractors when required Provide guidance to ensure alignment with platform standards and architecture Maintain quality and consistency across all Databricks deliverables **Qualifications:** **Education & Experience** - Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field - 5+ years of experience in data engineering, data platform development, or analytics engineering - Proven experience with IoT/OT data ingestion and industrial data protocols as well as hybrid SAP + cloud analytics architectures. **Technical Skills** - Strong SQL and Python for data engineering. - Strong hands-on experience with Databricks (must-have) - Proficiency in SQL and Python - Experience building scalable data pipelines (ETL/ELT) - Experience ingesting data from APIs, streaming platforms (e.g., Kafka), or similar technologies - Experience working with cloud-based data platforms (Azure preferred) - Experience with version control tools such as GitHub **Nice to Have** - Experience with SAP S/4HANA data structures and integration patterns (ODP, CDS views, SAP BTP services). - Prior work integrating SAP with cloud lakehouse platforms. - Hands‑on experience with SAP Datasphere modelling and data integration. - Familiarity with cloud platforms (Azure, AWS, or GCP). - Knowledge of streaming technologies (Kafka, Kinesis, or equivalent). - Understanding of LLMs, embeddings, and RAG architectures. - Experience building pipelines for unstructured data (documents, images, logs). - Exposure to ML lifecycle tooling (MLflow, SageMaker, Vertex AI, or Databricks ML). <