Description

You will own the end-to-end data lifecycle, bridging data engineering and machine learning.

This role is hybrid.

Responsibilities

• Develop and maintain data pipelines using AWS Glue for ingestion, transformation, and integration.
• Design and implement a data lake architecture utilizing Snowflake for scalable storage and processing.
• Utilize Python and Spark for data preprocessing, feature engineering, and model development.
• Leverage SageMaker to train, deploy, and monitor machine learning models in production.
• Conduct exploratory data analysis and data visualization to communicate findings to stakeholders.

Required Skills

• 5+ years of professional experience in data engineering or data science.
• Expertise with AWS Glue for ETL processes.
• Proficiency in Snowflake for data warehousing and lake architecture.
• Strong command of Python for data manipulation and ML.
• Experience using Spark for large-scale data processing.
• Hands-on experience with SageMaker for ML lifecycle management.
• Ability to ensure data quality, integrity, and security.

Key Skills

Education

Any Graduate