ML Engineer

Job summary

You will design and operationalize feature stores while managing Unity Catalog to secure data access across teams. Additionally, you will build efficient data pipelines and collaborate with cross-functional teams to integrate Azure-based data solutions.

Job details

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 with Java 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

What you’ll do

You will design and operationalize feature stores while managing Unity Catalog to secure data access across teams. Additionally, you will build efficient data pipelines and collaborate with cross-functional teams to integrate Azure-based data solutions.

Requirements

The role requires strong experience with Databricks Unity Catalog, Feature Store, and scalable ETL pipeline development. Proficiency in Python, Java, and Spark is essential, along with familiarity with Azure tools and container management via AKS.

Listed skills

• Splunk · Preferred
• Machine learning · Preferred
• Java · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Unity Catalog
• Databricks
• Feature Store
• Python
• Java
• Spark
• Azure Cosmos DB
• ETL Pipelines
• Machine Learning
• Splunk
• Datadog
• AKS
• ACR
• Data Engineering
• Cloud Infrastructure

Job areas

• Technology
• Software
• Data & Analytics
• Engineering
• Consulting