Description

  • Set up and manage Unity Catalog in Databricks to organize and secure data access across teams.
  • Design and operationalize Feature Stores to support machine learning models in production.
  • Build efficient data pipelines to process and serve features to ML workflows.
  • Collaborate with teams using Databricks, Azure Cosmos DB, and other Azure tools to integrate data solutions.
  • Monitor and optimize the performance of pipelines and feature stores.

 

YOUR PROFILE

  • 5 years of experience as a ML Engineer or similar roles.
  • Strong experience with Unity Catalog in Databricks for managing data assets and access control.
  • Hands-on experience working with Databricks Feature Store or similar solutions.
  • Knowledge of building and maintaining scalable ETL pipelines in Databricks
  • Familiarity with Azure tools like Azure Cosmos DB and ACR
  • Understanding of machine learning workflows and how feature stores fit into the pipeline.
  • Strong problem-solving skills and a collaborative mindset.
  • Proficiency using Java (specifically Java APIM) to deploy Machine Learning Models.
  • Proficiency in Python and Spark for data engineering tasks.
  • Experience with monitoring tools like Splunk or Datadog to ensure system reliability.
  • Familiarity with AKS for deploying and managing containers.
  • Advanced English

Key Skills
Education

Any Gradute

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