We are excited to invite applications for the Data Engineer Internship, commencing June 15th, 2026. This internship offers a fantastic opportunity for curious and motivated individuals to gain hands-on experience in designing, developing, and optimizing modern data platforms that drive analytics, experimentation, and AI-driven workflows.
The internship is structured to provide exposure to cloud data ecosystems such as Databricks, Snowflake, AWS, Azure, and GCP. Interns will have the chance to engage with real-world ETL/ELT pipelines, data transformations, and AI use cases.
Internship Details:
- Location: Conway, Arkansas
- Work Commitment: 20-25 hours/week during the semester, up to 40 hours/week during breaks and summer
- Anticipated Graduation: Between December 2026 - May 2027
Key Responsibilities:
- Assist in building and maintaining batch and streaming data pipelines using tools such as Spark, Databricks, and Snowflake.
- Support the development of ETL/ELT workflows utilizing orchestration tools like Apache Airflow and dbt.
- Help ingest structured and semi-structured data from various sources, including S3 and APIs.
- Write and maintain SQL and Python-based transformations for data cleaning and aggregation.
- Participate in implementing data quality checks and ensuring data reliability.
- Collaborate with data engineers, analysts, and data scientists to understand dataset consumption for analytics models.
- Assist in preparing datasets for AI/ML pipelines and automation processes.
- Explore AI agent interactions with data platforms under guidance from senior team members.
- Contribute to documentation on data flows and pipeline logic for team knowledge sharing.
- Follow data modeling, governance, and privacy best practices.
- Support version control and deployment processes using Git.
Required Qualifications:
- Pursuing a Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Information Systems, or related fields.
- Basic proficiency in SQL and familiarity with Python for data manipulation.
- Introductory understanding of data engineering concepts like ETL/ELT and data lakes.
- Exposure to at least one cloud platform (AWS, Azure, GCP).
- Strong willingness to learn and collaborate in a team environment.
- Clear communication skills with great attention to detail.
Preferred Qualifications:
- Experience with Databricks, Snowflake, or BigQuery through academic or personal projects.
- Familiarity with Apache Spark or workflow orchestration tools.
- Understanding of common data formats such as Parquet or JSON.
- Exposure to data privacy concepts like PII and GDPR.
- Experience with version control systems such as GitHub.
We encourage you to apply for this unique opportunity to gain valuable experience in the fast-evolving field of data engineering!