Job Requirements
We are looking for an accomplished
Enterprise Architect to lead the architectural design and technology strategy for a next-generation
Semiconductor Equipment Data & Analytics Platform.
This platform will manage and analyze
very large volumes of manufacturing equipment data—including process logs, alarms, events, and operational data—generated by advanced semiconductor tools such as plasma etch equipment with multiple process chambers. The system enables
process engineers and domain experts (non-programmers) to perform exploratory data analysis, equipment and chamber matching, apply statistical and AI/ML models, and create shareable dashboards through
web-based applications.
As an Enterprise Architect, you will define the
target architecture, drive
technology selection, and ensure the platform is
scalable, performant, secure, and maintainable in an on-premises, containerized environment. You will work closely with process engineering, data science, software engineering, and infrastructure teams, acting as the
technical authority across the program.
This role is well suited for architects with strong experience in
data platforms, analytics systems, and cloud-native architectures, ideally within
semiconductor manufacturing or other industrial, high-throughput domains
Objectives
- Define and own the end-to-end enterprise architecture for a large-scale semiconductor equipment data analysis platform as high-performance web application.
- Design a scalable architecture capable of handling hundreds of data-producing equipment chambers, large daily data volumes, and long data retention.
- Establish a robust AI/ML and MLOps architecture supporting model development, testing, deployment, and lifecycle management.
- Ensure seamless integration of data ingestion, analytics, ML, and BI/dashboarding capabilities.
- Provide architectural leadership, governance, and long-term roadmap alignment.
Responsibilities
- Define logical, physical, and deployment architectures across data, application, and presentation layers.
- Develop and maintain reference architectures, architectural principles, and design guidelines.
- Evaluate and select technologies, platforms, and frameworks aligned with business and technical requirements.
- Ensure architectural consistency across teams.
- Define backend service architecture, APIs, and analytics services.
- Drive design decisions for high-performance web-based UI/UX, including interactive visualizations and dashboards.
- Guide strategies for client-side vs server-side computation, including in-browser analytics where appropriate.
- Architect solutions for on-prem, containerized environments (Kubernetes / OpenShift).
- Ensure scalability, availability, observability, and operational readiness.
- Address security, access control, data governance, and compliance requirements.
- Act as the technical authority for architectural decisions and design reviews.
- Collaborate with cross-functional stakeholders and customers.
- Clearly communicate architectural vision, trade-offs, and decisions to both technical and non-technical audiences.
- Mentor engineering teams and influence best practices.
Work Experience
Must Have
- 15+ years of experience in enterprise, solution, or platform architecture roles.
- Proven track record designing large-scale, data-intensive systems.
- Strong understanding of distributed systems, scalability, and performance engineering.
- Experience designing web-based analytics platforms and backend services.
- Strong API design experience (REST, data APIs).
- Understanding of high-volume data delivery to web applications.
- Ability to translate complex business and domain requirements into robust technical architecture.
- Strong documentation, presentation, and stakeholder management skills
Good to Have
- Experience in semiconductor manufacturing, industrial automation, or similar high-throughput environments.
- Familiarity with equipment data, process logs, alarms/events, or chamber-level analysis.
- Knowledge of modern analytics ecosystems (e.g., columnar formats, query engines, ML platforms).
- Exposure to advanced visualization, interactive analytics, or browser-based computation.
- Experience participating in technology evaluations, RFPs, or vendor selection processes.
