We are seeking an

ML Dev Engineer with expertise in

Databricks Unity Catalog and Feature Store to build and maintain scalable data and machine learning foundations. The role focuses on data organization, access management, feature engineering, and supporting production ML workflows.

Key Responsibilities
Set up and manage

Databricks Unity Catalog for data organization and secure access control.
Design and operationalize

Feature Stores to support production ML models.
Build and maintain efficient data/ETL pipelines to process and serve ML features.
Integrate data solutions across

Databricks, Azure Cosmos DB, ACR

, and other Azure services.
Monitor, troubleshoot, and optimize pipeline and Feature Store performance.
Collaborate with data engineering and ML teams to support production workflows.

Required Skills & Experience




Robust hands-on experience with

Databricks Unity Catalog

, including data asset management and access control.
Experience with

Databricks Feature Store or similar solutions.
Solid knowledge of scalable

ETL pipelines in Databricks

.
Proficiency in

Python, Spark, and Java

.
Familiarity with

Azure Cosmos DB and Azure Container Registry (ACR)

.
Understanding of

ML workflows and Feature Store architecture

.
Experience with monitoring tools such as

Splunk or Datadog

.
Familiarity with

AKS for container deployment and management.
Robust problem-solving and collaboration skills.

📌 Machine Learning Specialist Vancouver
🏢 Querentia
📍 Vancouver