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 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 &nbs