Description

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

This role is on-site.

Responsibilities

• Build and optimize data pipelines using AWS Glue, Lambda, S3, EMR, and Step Functions.
• Develop and operationalize ML pipelines on SageMaker, covering preprocessing, training, deployment, and monitoring.
• Implement data ingestion frameworks for structured and unstructured data via 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 data science roles.
• Strong proficiency in Python, PySpark, and Pandas for large-scale data processing.
• Expertise in SQL and data architecture principles.
• Hands-on experience with AWS services: Glue, Lambda, S3, EMR, Step Functions, SageMaker, Redshift, Athena, and DynamoDB.
• Practical knowledge of MLOps practices, including model versioning and experiment tracking.
• Ability to productionize machine learning models and integrate them into data pipelines.
• Bachelor's degree in a relevant field (Any Graduate).

Key Skills

Education

Any Gradute