Overview: We’re looking for a highly motivated and technically proficient Data Engineering Intern to join our team. This internship is designed to provide real-world, impactful experience far beyond observational work. You will contribute directly to building and optimizing our data infrastructure, focusing on pipeline robustness, warehouse performance, and data reliability. Your efforts will play a vital role in enabling our Data Science, Business Intelligence, and Product teams to make informed, data-driven decisions.

Key Responsibilities:

• Pipeline Development & Maintenance: Build and maintain robust ETL/ELT pipelines to ingest data into our Amazon Redshift warehouse.
• Database Management & Optimization: Handle schema design, data modeling, and SQL performance tuning on Redshift and Postgres.
• Troubleshooting & Resolution: Investigate and resolve data integrity issues, pipeline failures, and performance bottlenecks.
• Data Loading & Quality Assurance: Oversee data ingestion, implement quality checks, and ensure data accuracy and integrity.
• BI & Analytics Enablement: Collaborate with analysts to structure data for seamless reporting in Tableau, Metabase, or similar BI tools.

Qualifications:

• Currently pursuing a Bachelor's or Master's in Computer Science, Information Systems, Data Science, or a related quantitative field.
• Strong foundational knowledge of computer science and algorithms.

Must-Have Technical Skills:

• Amazon Redshift: Strong understanding of columnar storage, query optimization using EXPLAIN plans, and COPY-based data ingestion.
• Advanced SQL: Ability to write and optimize complex queries using joins, CTEs, window functions, and aggregations.
• Database Design: Practical knowledge of schema design and data modeling (e.g., normalization, star/snowflake schemas).
• ETL/ELT Concepts: Sound grasp of designing pipelines for data movement from source to warehouse.
• Scripting (Python preferred): Proficient in scripting for data manipulation, automation, or API interaction.

Preferred Skills:

• Other AWS Services: Experience with S3, Glue, Lambda, or RDS.
• Relational Databases: Exposure to PostgreSQL or MySQL.
• NoSQL: Understanding of NoSQL systems such as DynamoDB or MongoDB.
• BI Tools: Experience with Tableau, Metabase, Looker, or Power BI.
• Data Science Curiosity: Interest in tools like Pandas or platforms like SageMaker.
• Version Control: Experience with Git and collaborative coding workflows