The Data Engineer Internship will begin in May and end in August of 2027. As a Data Engineer Intern, you will gain hands-on experience building and optimizing cloud-native data pipelines on AWS, working with AI/ML data workflows, and leveraging modern big data technologies including Spark, PySpark, PostgreSQL, and Amazon Redshift. What You’ll Do & Learn You will assist with the design, development, testing, and deployment of scalable data pipelines and cloud-based data solutions on AWS. Working alongside experienced engineers and data scientists, you will help build and maintain ETL/ELT workflows using Python, Spark, and PySpark to process large-scale datasets. You will gain exposure to AI/ML data preparation, helping ensure that data is clean, structured, and optimized for analytics and generative AI workloads. In addition, you will be responsible for: • Building and maintaining data pipelines using Python, Apache Spark, and PySpark to ingest, transform, and distribute data from multiple sources across the enterprise • Collaborating with cross-functional teams including data scientists, ML engineers, and business analysts to gather requirements and design data models using PostgreSQL and Amazon Redshift columnar databases • Developing and optimizing ETL/ELT processes using AWS services (Glue, Lambda, S3, EMR, Redshift) and supporting AI/ML data pipelines to prepare datasets for machine learning models and generative AI applications • Writing and optimizing complex SQL queries against PostgreSQL and Amazon Redshift to support analytics, reporting, and data-driven decision-making • Assisting with data quality monitoring, data governance, and implementing observability practices across data pipelines • Supporting the integration of AI-powered automation and intelligent data workflows to improve data processing efficiency • Learning and using AWS AI services (such as Amazon Kiro, Amazon Quick, and Amazon Q) to build, enhance, and maintain the data solutions and pipelines you are developing