Data Engineering Intern/Assistant Executive (Digital Transformation)

RySense Ltd.; Singapore; On-site; SGD 16,000 - 23,000; Part time

Job summary

RySense Ltd. in Singapore is seeking a driven Data Engineering Intern to help power our data ecosystem. You’ll build the backbone of our data infrastructure, designing robust pipelines and ensuring data quality.

You'll learn how Lakehouse architectures, orchestration patterns, and data governance underpin AI systems, with mentorship from senior engineers and exposure to modern data tools.

Qualifications

• Bachelor’s degree in CS, Data Eng, or related field.
• Strong Python skills with clean, efficient code.
• Solid understanding of data structures, algorithms and Data Lakehouse.
• Familiarity with Git and version control best practices.
• Curious, growth-oriented mindset to understand how data powers scalable AI.

Responsibilities

• Build and maintain ETL/ELT pipelines to ingest data into our data warehouse/lake.
• Manage data in Object Storage with ACID compliance and scalable metadata handling.
• Develop Python scripts to clean, validate, and transform raw data.
• Maintain data governance, including dataset registration and lineage documentation.
• Document data schemas, dictionaries, and pipeline architecture.
• Prepare high-quality datasets for model training and vector databases.
• Support deployment and monitoring of basic ML models or LLM applications.

Skills

Python

Data structures

Git

Growth mindset

Education

Bachelor’s degree in Computer Science, Data Engineering, Data Science, or related field

Tools

Docker

Dagster

Airflow

Delta Lake

Apache Iceberg

Job description

RySense is a Singapore-based research organisation. We focus on conducting research to understand what people in Singapore think, feel and do. Our primary areas of interest include social trends and public opinion. Using robust approaches, we seek to generate reliable insights about the Singapore landscape which reflect both subtle nuances and macro perspectives.

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.
• 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).