Process Intelligence Engineer
Applied Materials is hiring a Process Intelligence Engineer to design and maintain data modeling and analytics solutions for semiconductor manufacturing operations. The role involves building scalable ETL pipelines, developing dashboards using Power BI/Tableau, and conducting statistical analysis to support logistics and supply chain decision-making. Candidates will leverage SQL and Python to perform diagnostic analysis and build simulation models for operational optimization. This position focuses on translating complex business problems into actionable data-driven insights within a global industrial environment.
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Experience
4-7 years
Function
Engineering
Work mode
Onsite, Singapore
Company
Tier 2
What you will work on
Applied Materials is hiring a Process Intelligence Engineer to design and maintain data modeling and analytics solutions for semiconductor manufacturing operations. The role involves building scalable ETL pipelines, developing dashboards using Power BI/Tableau, and conducting statistical analysis to support logistics and supply chain decision-making. Candidates will leverage SQL and Python to perform diagnostic analysis and build simulation models for operational optimization. This position focuses on translating complex business problems into actionable data-driven insights within a global industrial environment.
TAL's take
Solid mid-level engineering role at a major global industrial leader with defined analytical scope.
Well-defined responsibilities bridging industrial engineering, data analytics, and business intelligence.
Salaries at Applied Materials
19.6 LPA average
Based on 22 Grapevine salary entries for Applied Materials.
Engineering
2 - 4 years | E2
23 LPA average
Range: 19 - 28 LPA
Engineering
4 - 6 years | E2
16 LPA average
Range: 16 - 16 LPA
Engineering
6 - 8 years | B3
16 LPA average
Range: 16 - 16 LPA
Engineering
8 - 10 years | B3
32 LPA average
Range: 32 - 32 LPA
Must haves
- Bachelor's degree in Industrial Engineering, CS, Business Analytics, or related field
- 4-7 years experience in analytics, dashboarding, or data engineering
- Proficiency in SQL and Python for data analysis
- Experience with Power BI or Tableau
- Experience building ETL pipelines and data models
Tools and skills
Nice to have: azure, databricks, snowflake, aws, gcp, dax.
About the company
Global semiconductor equipment leader, but not a software-native tech brand.
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