Engineer, Data Analytics Engineering

Sandisk; Batu Kawan; On-site; MYR 120,821 - 181,232; Full time

Job summary

Sandisk in Malaysia is seeking a Junior Data Management Engineer eager to work on machine learning and computer vision applications within data engineering tasks. This entry-level role will provide exposure to data pipelines optimization and ML model deployment, offering a chance to collaborate with multiple stakeholders to enhance manufacturing processes.

The ideal candidate will hold a relevant Bachelor’s degree and has up to two years of experience. Proficiency in SQL and Python is crucial, as well as skills in ML frameworks and deployment tools.

Qualifications

• 0–2 years of experience in data analytics, data engineering, or machine learning.
• Proficiency in SQL for data querying and manipulation.
• Strong programming skills in Python.
• Experience with model deployment in production environments.

Responsibilities

• Work with stakeholders to resolve manufacturing process related problems.
• Apply computer vision algorithms to inspect production lines for defects.
• Build, train, and validate machine learning models.
• Deploy trained models into production environments.
• Design scalable GenAI applications using LLMs.

Skills

Data preprocessing and feature engineering techniques

Ability to interpret and visualize data insights

Strong analytical and problem-solving mindset

Effective written and verbal communication

Team collaboration and proactive learning attitude

Education

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

Tools

SQL

Python

Spark

ML frameworks (PyTorch, TensorFlow/Keras)

CV libraries (OpenCV, Detectron2, MMDetection)

Docker

Kubernetes

Job description

Job Description

We are looking for a curious and motivated Junior Data Management Engineer with foundational experience in machine learning, Computer Vision and GenAI to join our growing data team. This role is perfect for someone early in their career who is eager to contribute to data engineering tasks while gaining hands‑on exposure to machine learning workflows. You'll help ensure data pipelines are optimized for analytics and ML model development, supporting both operational and strategic decision‑making.

Essential Duties And Responsibilities

• Work with multiple stakeholders to identify, diagnose and resolve manufacturing process related problems such as quality excursions, manufacturing excellence and cost improvement.
• Apply computer vision algorithms to inspect production lines for defects, improve yield, and enhance overall quality control. Leverage techniques such as object detection, image segmentation, and defect localization to identify issues early in the manufacturing process.
• Build, train, and validate machine learning models to address complex manufacturing challenges, such as defect detection, process optimization, and performance prediction.
• Able to deploy trained models into production environments, including APIs, edge devices, or embedded systems. Ensure seamless integration with manufacturing systems and establish real‑time monitoring for model performance.
• Design and develop scalable GenAI applications using LLMs tailored to manufacturing use cases.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, Statistics, or a related field.
• 0–2 years of experience in data analytics, data engineering, or machine learning.
• Proficiency in SQL for data querying and manipulation.
• Strong programming skills in Python; experience with SQL, Spark, or other big data tools for processing large‑scale manufacturing/sensor datasets.
• Proficiency in ML frameworks (PyTorch, TensorFlow/Keras) and CV libraries (OpenCV, Detectron2, MMDetection).
• Experience with model deployment in production (eg, Docker, Kubernetes, edge devices) for real‑time or batch inference.

Preferred

• Exposure to building or assisting in ML models using libraries like scikit‑learn or TensorFlow.
• Experience with data visualization tools (e.g., Spotfire, Power BI, Tableau).
• Familiarity with version control tools such as Git.
• Exposure to ML lifecycle tools like MLflow, DVC, or Kubeflow.

Skills

• Data preprocessing and feature engineering techniques.
• Ability to interpret and visualize data insights.
• Strong analytical and problem‑solving mindset.
• Effective written and verbal communication.
• Team collaboration and a proactive learning attitude.