About the role

Data and ML platform engineer building streaming, inference, and ML infrastructure for Homebase’s workforce management app. Owning data pipelines, platform operations, and model serving.

Responsibilities

Build and run CDC and streaming pipelines using Kafka, Debezium, Spark Streaming, and Delta
Own replay, restream, and ingestion SLAs
Manage Databricks, Unity Catalog, warehouses, and Terraform
Implement CI/CD and DAB deployments for data infrastructure
Build gRPC services and move batch workloads to real-time inference
Make LLM latency and cost visible
Evolve the feature store and MLflow practice
Partner with data science to take proofs of concept to production
Use AI-assisted tools to build, test, and operate systems
Collaborate with data engineering, data science, and product engineering
Participate in the on-call rotation and improve system reliability

Production experience with distributed streaming systems, including Kafka and CDC
Hands-on Spark and Delta experience
Strong Python and SQL skills
Experience with container infrastructure such as Docker, ECS, or Kubernetes
Infrastructure-as-code experience, ideally Terraform
Solid data modeling fundamentals
Comfort being part of an on-call schedule
Experience experimenting with AI tools in an engineering workflow and sharing learnings
Comfort moving quickly through ambiguity with clear communication across technical and non-technical teams
Ownership mindset and accountability for systems built
Ability to collaborate in person at the Toronto hub every Tuesday and Wednesday
Bonus: Unity Catalog governance
Bonus: MLflow and feature stores
Bonus: LLM-serving experience
Bonus: Protobuf event contracts
Bonus: Postgres internals