Total 5 data entry job vacancies in Batu Kawan

Act as the key local expert and lead user for planning-relevant master data, ensuring high consistency, quality and reliability of data used in Blue Yonder to support holistic planning of the semiconductor production network.

Serve as a critical interface between business, specialty departments, IT, and external IT service providers, driving further development, stabilization and continuous improvement of master data and planning IT solutions.

Utilize cost advantages of LCL.

• Drive end‑to‑end planning master-data quality and governance, ensuring reliable, usable data across business functions
• Act as technical lead for analytics and data solutions (Power BI, SQL Oracle/MSSQL, KNIME, databases, web/UI tools)
• Responsible for maintenance and enhancement of master data applications and UIs (e.g. Tivity) aligned with business needs
• Drive dashboards and advanced analytics to enable planning excellence, transparency, and self‑service insights
• Lead root cause analysis of complex data issues, implementing sustainable, preventive solutions
• Define, monitor and improve data governance frameworks, validation rules, workflows, KPIs and operating models
• Identify data quality trends and drive automation, structural improvements and scalability
• Ensure data solutions effectively support planning processes (demand planning, logistics interfaces, master data flows)
• Manage global and local stakeholder expectations, priorities and dependencies with clear, business‑oriented communication
• Motivate and develop the team by setting technical standards, best practices and ways of working
• Foster a culture of ownership, continuous improvement, analytical thinking and knowledge sharing
• Lead and contribute to PJM1‑SO projects, driving innovation, efficiency and advanced analytics adoption

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.

ESSENTIAL DUTIES AND RESPONSIBILITIES: 

• Perform data mining and analytics for Quality Assurance (QA) in semiconductor Assembly & Testing processes
• Understand QA business workflows (e.g., SPC, 8D, defect management) and translate business needs into scalable data solutions
• Design and develop end-to-end data pipelines, including data ingestion, transformation, modeling, and visualization
• Develop analytical methods such as anomaly detection, trend analysis, and predictive modeling to enhance quality control
• Drive the transformation of quality management from manual processes to automated and intelligent systems
• Develop dashboards, reports, and automated QA-related business applications
• Collaborate with IT, data teams, and business stakeholders to integrate data platforms with operational systems
• Explore and implement AI/GenAI use cases in quality engineering, such as intelligent insights and automated reporting

Join our exciting team as a Data Engineer with the following job scope:

• Architect and implement data solutions on our large-scale data platform Hadoop using PySpark.
• Write complex and efficient SQL queries to extract and transform data from various sources.
• Design, build, and maintain scalable and reliable data pipelines using tools like Apache Airflow and Kafka.
• Develop and optimize data processing jobs on the Microsoft Azure cloud platform, leveraging services such as Azure Data Lake, Databricks, and Azure Data Factory.
• Collaborate with data analysts and other stakeholders to understand data requirements and deliver high-quality data products.
• Ensure data quality and integrity across all systems.
• Monitor, troubleshoot, and optimize the performance of our data infrastructure.
• Stay up-to-date with the latest trends and technologies in the Big Data space.

The Logistics Center of Excellence (COE) is the global hub driving logistics process excellence and digital transformation. The team focuses on process optimization, digital enablement, and advanced analytics to support data‑driven decision‑making across the logistics organization.

This role plays a key individual‑contributor and technical leadership role in delivering BI and AI solutions. You will work closely with logistics business stakeholders to identify requirements, design and develop BI/AI solutions, and lead capability building within the COE team.

ESSENTIAL DUTIES AND RESPONSIBILITIES: 

• Act as an independent BI/AI partner to logistics business teams, understanding requirements and proactively proposing data, BI, and AI solutions to address business needs.
• Design, develop, and maintain BI and AI solutions using Power BI and approved company AI and analytics platforms.
• Translate business problems into analytical models, dashboards, and insights that support operational and strategic decision‑making.
• Own end‑to‑end delivery of digital tasks, including requirement clarification, solution development, testing, deployment, and post‑deployment support.
• Communicate effectively with business stakeholders to track progress, provide status updates, and manage expectations throughout the project lifecycle.
• Provide technical leadership in BI/AI within the Logistics COE, including knowledge sharing, best practices, and hands‑on training for team members.
• Support broader logistics digital transformation initiatives and complete additional assignments as directed by the supervisor.