About this role
Job Requirements
Summary
We are seeking an experienced Data Architect to design and govern data architecture for a large-scale Semiconductor Equipment Data Analysis Platform. The platform handles high-volume equipment data—including process logs, alarm logs, event logs, and operational time-series data—generated by advanced semiconductor manufacturing tools such as plasma etch equipment with multiple chambers.
The Data Architect will be responsible for defining data models, data organization, partitioning strategies, and analytical data structures that enable efficient exploratory data analysis (EDA), AI/ML workflows, and business intelligence dashboards.
This role works closely with process engineers, data scientists, ETL engineers, and platform architects, ensuring that complex equipment data is transformed into analytics-ready, high-quality datasets usable by non-programmer domain experts. This is a hands-on architectural role requiring deep expertise in modern data platforms, large-scale time-series data, and analytical data modeling, preferably in semiconductor manufacturing or other industrial environments.
Objectives
- Design a scalable, analytics-ready data architecture for high-volume semiconductor equipment data.
- Enable self-service exploratory data analysis for process engineers and domain experts.
- Define data modeling and partitioning strategies that support chamber-level and equipment-level analytics.
- Ensure data quality, consistency, and governance across the data lifecycle.
- Support AI/ML and statistical modeling through well-designed datasets and metadata.
- Optimize data storage, query performance, and long-term retention.
Responsibilities
- Design logical and physical data models for equipment, chamber, process, recipe, and time-series data.
- Define naming conventions, data organization, and dataset structures aligned with analytics use cases.
- Define data partitioning strategies (e.g., by equipment, chamber, date, process step, recipe).
- Balance query performance, storage efficiency, and retention requirements.
- Define strategies for schema evolution and backward compatibility.
Understanding of partitioning
Work Experience
Required Skills
- 8+ years of experience in data architecture, data modeling, or large-scale analytics platforms.
- Strong expertise in analytical data modeling (star/snowflake, wide tables, time-series models).
- Proven experience designing high-volume, time-series or log-based datasets.
- Hands-on experience with data lakes or lakehouse architectures.
- Strong understanding of columnar data formats and analytical storage.
- Experience optimizing data layouts for large-scale analytical queries.
Other facts
About Quest Global
We are Quest Global. We’re in the business of engineering, but what we’re really building is a brighter future. It’s not just what we do, but why we do it that makes us different. We believe engineering has the unique opportunity to solve the problems of today that stand in the way of tomorrow. For more than 25 years, we have strived to be the most trusted partner for the world’s hardest engineering problems. As a global organization headquartered in Singapore, we live and work in 18 countries, with 93 global delivery centers and offices, driven by 21,000+ extraordinary employees who make the impossible possible every day.
Quest Global delivers world-class end-to-end engineering solutions by leveraging our deep industry knowledge and digital expertise. By bringing together technologies and industries, alongside the contributions of diverse individuals and their areas of expertise, we are able to solve problems better, faster. This multi-dimensional approach enables us to solve the most critical and large-scale challenges across the aerospace & defense, automotive, energy, hi-tech, healthcare, medical devices, rail and semiconductor industries.
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What you'll do
- The Data Architect will design logical and physical data models for high-volume semiconductor equipment data and define data organization and partitioning strategies. This role ensures that complex equipment data is transformed into analytics-ready datasets for use by domain experts.
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