[KYDP 2025 - Cohort 5] Junior Data Engineer

TalentLabs; Selangor; On-site; MYR 100,000 - 150,000; Full time

Job summary

A tech consulting firm in Malaysia is looking for a Junior Data Engineer to assist in developing and maintaining AI-powered solutions. The role involves data collection, cleaning, and supporting machine learning model development. Ideal candidates should be familiar with data analytics tools and have a basic understanding of machine learning concepts. This is a great opportunity for those looking to start a career in data science.

Qualifications

• Familiarity with data analytics tools and techniques.
• Basic understanding of machine learning concepts.
• Ability to present data findings clearly.

Responsibilities

• Collect, clean, and prepare datasets for analysis.
• Support the development and testing of machine learning models.
• Analyze data to uncover trends and patterns.
• Assist in the deployment of AI models.

Skills

Data cleansing and preparation

Machine learning support

Data analysis

Data visualization

Job description

[KYDP 2025 - Cohort 5] Junior Data Engineer

Open To: Data Analytics, Prompt Engineering & Machine Learning

OJT Period:

[Cohort 5] - 1 October to 31 December

As a Data & AI Specialist, you will support the development, deployment, and maintenance of data-driven and AI-powered solutions that help drive business insights and automation. Your role includes working with structured and unstructured data, assisting in model development, and collaborating with teams to implement machine learning and data analytics pipelines. Some examples of your responsibilities include (but are not limited to) the following:

Collecting, cleaning, and preparing datasets for analysis and model training

Supporting the development and testing of machine learning models under the guidance of senior data scientists or AI engineers

Analyzing data to uncover trends and patterns, and presenting findings in a clear, actionable manner

Assisting in the deployment of AI models and data pipelines into production environments

Documenting processes, methodologies, and results to ensure knowledge transfer and reproducibility

Using data visualization tools to communicate insights and performance of models