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ML ( Machine Learning ) Engineers with Unity Catalog working with Databricks at Unity, Wisconsin, USA
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From:

Rahaman Mohammed,

Techgene Solutions

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ML ( Machine Learning ) Engineers with Unity Catalog working with Databricks

Interview Mode : Webcam

Seattle based Client

Remote is OK for strong candidate / Hybrid working is good to have with -3 days office in Seattle-WA

Need to have experience  to feature store and operationalization of a feature store

We are looking for an ML engineer with expertise in Unity Catalog and Feature Store in Databricks to help us build and maintain a solid foundation for our data and machine learning workflows. You will work on organizing data, managing access, and enabling machine learning models to operate efficiently in production

What You Will Do -

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

What We are Looking For -

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 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

Keywords: machine learning database rlang Washington
ML ( Machine Learning ) Engineers with Unity Catalog working with Databricks
[email protected]
[email protected]
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03:02 AM 07-Jan-25


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