Data Engineering Intern
Summary
Rysense is hiring a Data Engineering Intern (on-site, Singapore) to build and maintain ETL/ELT pipelines, manage data in object storage with a lakehouse architecture, and prepare datasets for AI/ML work. Core stack includes Python, Git, and modern data tools like Dagster, Airflow, Delta Lake, and Apache Iceberg.
ABOUT THE ROLE – EXPLORE, LEARN, AND MAKE AN IMPACT
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.
WHY JOIN US?
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.
WHAT YOU WILL DO
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.
WHAT ARE WE LOOKING FOR
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).