Description

Design and implement data architectures and machine learning pipelines within AWS and Snowflake environments.

Responsibilities

• Develop and maintain ETL pipelines using AWS Glue for data ingestion, transformation, and integration.
• Design scalable data lake architecture using Snowflake Data Lake, Snowflake data warehouse, and S3.
• Utilize Python and Spark for data preprocessing, feature engineering, and model development.
• Leverage SageMaker for model training, evaluation, deployment, and production monitoring.
• Conduct exploratory data analysis and visualization to communicate findings to stakeholders.

Required Skills

• 5-6 years of experience with AWS services, specifically Glue, S3, and SageMaker.
• Proficiency in Python for data manipulation, analysis, and modeling.
• Hands-on experience with Snowflake Data Lake and Snowflake data warehouse.
• Experience with Spark for distributed big data processing.
• Knowledge of machine learning algorithms including regression, classification, clustering, and recommendation systems.
• Experience deploying and monitoring machine learning models in production environments.
• Familiarity with MLOps practices, model versioning, and automated deployment pipelines.
• Working knowledge of SQL, NoSQL databases, and data warehousing concepts.
• Bachelor's degree or higher in Computer Science, Data Science, Statistics, or a related field.

Preferred Skills

• Certifications in AWS, Python, or Spark.
• Familiarity with Tableau for data visualization.

Key Skills

Education

ANY GRADUATE