Who you are

- Production experience with distributed streaming systems (Kafka and CDC)

- Hands-on Spark and Delta experience

- Robust Python and SQL
- Experience with container infrastructure (Docker, ECS, or Kubernetes)

- Infrastructure-as-code experience, ideally Terraform

- Solid data modeling fundamentals
- Comfort being part of an on-call schedule

- AI fluency: you already experiment with AI tools in your engineering workflow and can share what you learned

- Comfort moving quickly through ambiguity, with clear communication across technical and non-technical teams
- An ownership mindset: you stay close to the details and stand behind the systems you build
- The ability to collaborate in person at our Toronto hub every Tuesday and Wednesday, and to use that time for fast decisions and shared context

- Unity Catalog governance

- MLflow and feature stores
- LLM-serving experience

- Protobuf event contracts

- Postgres internals
- ? Belonging at Homebase - We're committed to fostering a welcoming space where every Homie can be their full self.

Experience comes in many forms—so if you're excited about this role, even if you don’t meet 100% of the qualifications, we encourage you to apply!

What the job involves

- Every data and AI feature at Homebase runs on the platform you’ll help build. Our AI-powered product work is moving fast, and the platform underneath it has to move faster. You’ll be one of two engineers who carry that build

- You’ll own how data gets in, how the platform runs, and how models and data get served back to the product, with real ownership from day one

- Own data-in. Build and run CDC and streaming pipelines (Kafka, Debezium,



Spark streaming into Delta), including replay, restream, and ingestion SLAs

- Own the data platform.

Manage

Databricks, Unity Catalog, warehouses, and Terraform, with CI/CD and DAB deploys for data infrastructure

- Build the serving layer. Ship gRPC services, move batch workloads to real-time inference, and make LLM latency and cost visible

- Grow the ML foundations. Evolve our feature store and MLflow practice, and work with data science to take proofs of concept to production

- Be AI-first in how you build. Use AI-assisted tools to build, test, and operate faster, experiment with new capabilities, and share what works with the team

- Partner across teams. Work with data engineering, data science, and product engineering to turn customer needs into reliable systems

- Share the on-call. Join the team’s on-call rotation, which has its own escalation path, and help make it quieter over time

The application process

- Meet the Talent Acquisition team, Mavel W

- Meet the Hiring Manager, Paul W

- Participate in technical interviews (live coding plus a data and ML platform design conversation)

- Background Check + Offer Stage

- Welcome to the team, Homie ?

Benefits

- Stock Options

- Paid parental leave
- Generous paid time off options

- Offsites, customer days & social events

- Work from Anywhere Month
- Medical, dental, and vision insurance

- 401(k) matching program

- Pre-tax commuter benefits

- Snacks + lunch on us at hub offices

- MasterClass at Work for ongoing learning

- Group health benefits coverage
- Group RRSP with employer match + TFSA

- Snacks + lunch on us at hub offices

- MasterClass at Work for ongoing learning

📌 Data & ML Platform Engineer (Toronto)
🏢 Homebase
📍 Toronto