Phaxis
Sr Machine Learning Engineer - Databricks & Snowflake
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Sr Machine Learning Engineer with a contract length of unspecified duration, offering $70 to $75 per hour. Key skills include Python, Databricks, Snowflake, MLflow, and advanced SQL, with a focus on machine learning model deployment and data pipeline optimization.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
600
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🗓️ - Date
April 25, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
New York, NY
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🧠 - Skills detailed
#MLflow #Pandas #Data Governance #NumPy #Databricks #Data Pipeline #Scala #Spark (Apache Spark) #Data Analysis #ML (Machine Learning) #Python #SQL (Structured Query Language) #Snowflake #"ETL (Extract #Transform #Load)"
Role description
Pay rate is $70 to $75 per hour
• Leverage strong expertise in Python (pandas, NumPy, scikit-learn) along with hands-on experience in Databricks, Snowflake, MLflow, Unity Catalog, and advanced SQL
• Build, train, validate, and deploy machine learning models for real-world business applications
• Perform exploratory data analysis (EDA) and feature engineering to improve model performance
• Apply statistical modeling and machine learning techniques (supervised and unsupervised)
• Productionize models using MLflow, including experiment tracking, versioning, and lifecycle management
• Develop CI/CD pipelines for ML workflows and monitor model performance in production
• Design and optimize scalable data pipelines using Databricks (Spark) in distributed environments
• Implement data governance, lineage, and access control using Unity Catalog
• Develop and optimize Snowflake data models; write advanced SQL for transformations and analytics
Pay rate is $70 to $75 per hour
• Leverage strong expertise in Python (pandas, NumPy, scikit-learn) along with hands-on experience in Databricks, Snowflake, MLflow, Unity Catalog, and advanced SQL
• Build, train, validate, and deploy machine learning models for real-world business applications
• Perform exploratory data analysis (EDA) and feature engineering to improve model performance
• Apply statistical modeling and machine learning techniques (supervised and unsupervised)
• Productionize models using MLflow, including experiment tracking, versioning, and lifecycle management
• Develop CI/CD pipelines for ML workflows and monitor model performance in production
• Design and optimize scalable data pipelines using Databricks (Spark) in distributed environments
• Implement data governance, lineage, and access control using Unity Catalog
• Develop and optimize Snowflake data models; write advanced SQL for transformations and analytics
