Description

You will design, build, and maintain scalable data pipelines and operationalize ML workflows on AWS.

This role is on-site.

Responsibilities

• Build and maintain scalable data pipelines using AWS Glue, Lambda, S3, EMR, and Step Functions.
• Develop and 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, including Pandas and PySpark.
• Deep expertise with AWS services: SageMaker, Glue, Lambda, S3, EMR, Step Functions, Redshift, Athena, and DynamoDB.
• Experience building reusable components for feature engineering, model versioning, and experiment tracking.
• Proficiency in SQL for large-scale data processing and analytics.
• Understanding of MLOps practices and data architecture principles.
• Bachelor's degree in a relevant technical field.

Key Skills

Education

Bachelor's degree