Data Engineering Intern

We're looking for a driven and curious Data Engineering Intern to help power our data ecosystem. In this role, you won't just be \"moving data\" - you'll be building the backbone of our data infrastructure. From designing robust data pipelines to ensuring high standards of data quality and reliability, you'll play a key role in making data usable, scalable, and impactful.

If you're a passionate coder who loves working with data and solving complex problems, this internship will accelerate your growth in the right direction.

By working closely with our AI/ML team, you'll gain firsthand insight into how well-engineered data fuels intelligent systems. This is a rare opportunity to bridge the gap between Data Engineering and Artificial Intelligence - gaining real-world, practical exposure to how clean, structured data evolves into powerful machine learning models.

You will learn:

• How to implement a Lakehouse architecture from scratch
• Orchestration patterns
• The importance of Data Governance in a professional AI environment

Supported by mentorship from senior engineers and data experts.

1. Data Engineering (Core Focus)

• Assist in building and maintaining ETL/ELT pipelines to ingest data into our data warehouse/lake.
• Manage and organize data in Object Storage, ensuring ACID compliance and scalable metadata handling.
• Develop Python scripts to clean, validate, and transform raw data.
• Maintain data governance practices, including dataset registration and lineage documentation.
• Document data schemas, dictionaries, and pipeline architecture clearly.
  

2. AI & Machine Learning Support (Growth Focus)

• Prepare high-quality datasets for model training and vector databases.
• Support deployment and monitoring of basic ML models or LLM applications.
  

The ideal candidate has the following qualities:

• Currently pursuing or recently graduated from a Bachelor's degree in Computer Science, Data Engineering, Data Science, or related field.
• Strong proficiency in Python, with the ability to write clean, efficient, and well-structured code.
• Solid understanding of data structures, algorithms, and the Data Lakehouse concept.
• Familiarity with Git and version control best practices.
• A curious and growth-oriented mindset, with a strong desire to understand how data engineering powers scalable AI and machine learning solutions.

While not required, the below will offer an additional advantage:

• Knowledge of containerization (Docker).
• Familiarity with Modern Data Stack tools (such as Dagster, Airflow, Delta Lake or Apache Iceberg).
• Basic understanding of Machine Learning workflows or LLMs (OpenAI, Ollama, HuggingFace).