Senior Data Platform Engineer

About this role

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

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).

• Ability to support model monitoring and AI observability.

About You:

• Hands-on and solution-oriented, with a strong sense of ownership

• Comfortable working across the full data lifecycle

• Strong problem-solving and analytical skills

• Effective communicator, able to collaborate with both technical and business teams

• Self-starter who thrives in a fast-paced, evolving environment

• Passionate about building modern data platforms that enable better decision-making

• Naturally take ownership and have a can-do attitude.

• You embody Riverside values of humility, adaptability, respect, and fun—and inspire your team to do the same.

• Be Bold & Courageous: Challenge the status quo, act with intent before conditions are perfect, and learn fast. Try new approaches, adjust quickly, and pursue win-win outcomes…