Description

• Design, build, and maintain scalable data pipelines using AWS services such as Glue, Lambda, S3, EMR, and Step Functions.
• Develop and operationalize ML pipelines on AWS SageMaker, including data preprocessing, model training, deployment, and monitoring.
• Collaborate with data scientists to productionize machine learning models and ensure efficient integration into data pipelines.
• Implement data ingestion frameworks for structured and unstructured data from multiple sources (batch and streaming).
• Optimize data storage and query performance using AWS services like Redshift, Athena, and DynamoDB.
• Build reusable components for feature engineering, model versioning, and experiment tracking within SageMaker ecosystem.
• Ensure data quality, governance, and security by implementing validation checks, encryption, and access controls.
• Use Python (PySpark/Pandas), SQL, and frameworks like Spark for large-scale data processing and analytics.
• Monitor and troubleshoot pipelines and ML workflows, ensuring high availability and performance reliability.
• Stay updated with AWS advancements and recommend improvements in data architecture, MLOps practices, and cost optimization

Key Skills

Education

Bachelor's degree