Applied Materials is the leader in materials engineering solutions that are at the foundation of virtually every new semiconductor and advanced display in the world. The technology we create is essential to advancing AI and accelerating the commercialization of next-generation chips. At Applied, we push the boundaries of science and engineering to deliver material innovation that changes the world.

We look forward to engaging with you on compelling topics about the semiconductor industry. We want to hear from you, but offensive comments that create an unpleasant environment for our community will be removed. Thanks for your understanding.

Key Responsibilities:

Partner with process engineers, and integration teams to understand wafer experiment workflows—including etch, deposition, and metrology steps—and translate experimental and yield-learning objectives into actionable data and reporting requirements.

Design, develop, and maintain metrics, dashboards, and analytical reports focused on process tool performance, recipe execution, chamber health, and wafer-level traceability, and using ClickHouse, SQL, and Python.

Write detailed specifications for new data pipelines to onboard additional metrology tools, sensors, or process data sources, collaborating with data engineering to ensure proper schema design, transformations, and quality checks within the AWS/Kafka/ClickHouse stack.

Validate and reconcile data from disparate systems (MES, tool logs, recipe management, metrology platforms, yield databases) to ensure accurate linkage at the wafer, lot, run, and step level within the technical data warehouse.

Develop reusable Python scripts and SQL queries for ad-hoc and recurring analyses such as DOE evaluation, process window characterization, virtual metrology exploration, and wafer-to-wafer or lot-to-lot comparisons.

Work within established data governance and access controls to ensure accuracy, timeliness, and appropriate confidentiality of all engineering reports, dashboards, and experimental data.

Create and maintain documentation for datasets, table schemas, KPIs, and pipeline logic—including data lineage, business definitions, and usage examples tailored to process contexts.

Train and support application developers and data analysts in using the data warehouse, BI tools, and self-service dashboards; gather feedback to continuously improve data products and user experience.

Collaborate with data scientists and ML engineers to operationalize predictive models by ensuring required features and labels are available, accurate, and well-documented in the warehouse.

Create automated workflows for data ingestion, cleansing, and integration of large-scale process and metrology datasets, leveraging Python, ETL orchestration tools, and cloud services on AWS.

Assess evolving reporting needs in the context of R&D priorities and technology roadmaps; propose and deliver appropriate solutions ranging from quick ad-hoc analyses to production-grade dashboards.

Generate internal documentation, presentations, and technical reports summarizing experimental results, data quality assessments, and analytics insights for cross-functional stakeholders.

Qualifications:

Bachelor’s degree in data science, computer science, engineering, or related field.

4+ years of experience in data analysis, preferably within a high-tech or manufacturing environment.

Demonstrated experience supporting business units with analytical solutions.

Preferred Attributes:

Strong attention to detail and organizational skills.

Excellent communication skills for presenting complex findings to stakeholders.

Ability to work independently and collaboratively in a fast-paced environment.

Proactive approach to process improvement and automation.
Software Development Process

JIRA, BitBucket