Business Intelligence Analyst

Applied Materials•Santa Clara, CA

About The Position

Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips – the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world – like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world. You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more. At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits .

Requirements

Ability to translate engineering workflows and business needs into data requirements and specifications.
Experience working directly with engineers, operations teams, or technical stakeholders.
Solid documentation, communication, and stakeholder engagement skills.
Experience designing and validating metrics, KPIs, and dashboards for operational or engineering environments.
Strong proficiency in SQL and Python for data analysis, automation, and pipeline support.
Experience with BI and visualization tools (Tableau, Power BI, Superset, Grafana, Looker, or similar).
Version control (Git), CI/CD for analytics, and data documentation tooling.
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.

Nice To Haves

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.

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.