Description

Design, build, and maintain scalable data pipelines and ML operations on AWS.

This role is on-site.

Responsibilities

• Build and maintain scalable data pipelines using AWS Glue, Lambda, S3, EMR, and Step Functions.
• Operationalize ML pipelines on AWS SageMaker, covering preprocessing, training, deployment, and monitoring.
• Implement data ingestion frameworks for structured and unstructured data from batch and streaming sources.
• Optimize data storage and query performance using Redshift, Athena, and DynamoDB.
• Ensure data quality, governance, and security through validation checks, encryption, and access controls.

Required Skills

• 7+ years of experience in data engineering or machine learning operations.
• Strong proficiency in Python with Pandas and PySpark for large-scale data processing.
• Deep experience with AWS services including Glue, Lambda, S3, EMR, Step Functions, Redshift, Athena, and DynamoDB.
• Hands-on experience with AWS SageMaker for building and deploying ML models.
• Proficient in SQL for data querying and analytics.
• Knowledge of data architecture and MLOps practices.
• Bachelor's degree in a relevant technical field.

Key Skills

Education

Bachelor's degree