Business Intelligence Analyst

Applied Materials•Austin, TX

1d•$122,000 - $168,000

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. The Business Analyst (B4) leads complex analytics initiatives that translate manufacturing and operational needs into actionable insights, scalable datasets, and intuitive dashboards. This role partners with stakeholders across manufacturing, quality, supply chain, and engineering to define requirements, build analytic strategies, and deliver self-service reporting using Tableau and Databricks, while applying AI-assisted techniques to accelerate insight generation and decision-making. The role requires specialized depth and/or breadth of expertise, independent execution, and the ability to lead projects or workstreams with moderate complexity.

Requirements

Experience with data modeling, metric governance, and semantic layers to enable trusted self-service analytics.
Exposure to AI/ML concepts (prompting patterns, LLM-assisted summarization/insight generation, basic model evaluation, or partnering with DS teams). [
Manufacturing systems familiarity
Interprets internal/external business challenges and recommends best practices to improve products, processes or services
May lead functional teams or projects with moderate resource requirements, risk, and/or complexity
Leads others to solve complex problems; uses sophisticated analytical thought to exercise judgment and identify innovative solutions
Impacts the achievement of customer, operational, project or service objectives; work is guided by functional policies
Ability to communicate complex concepts, negotiate tradeoffs, and influence stakeholders to align on definitions, metrics, and actions.

Responsibilities

Lead analytics initiatives end-to-end: Manage and facilitate significant analytic initiatives supporting strategic, operational, and quality/manufacturing projects; maintain reusable documentation, templates, and model artifacts.
Translate business needs into analytic solutions: Work with operational leaders to convert business needs into reporting and analytics strategies, including execution plans and resourcing for routine and project work.
Requirements & process mastery in manufacturing environments: Analyze and interpret complex manufacturing datasets, identify trends/patterns, and recommend actions that improve process performance, yield, cycle time, scrap, and compliance outcomes (as applicable to the domain).
Build dashboards and reporting products: Design and implement dashboards and ad-hoc reporting solutions; educate and train user communities on effective use of BI products.
Databricks-centric data work: Use Databricks (SQL/warehouses, curated tables/views) to enable governed, performant analytics consumption and scalable reporting.
Data integrity & validation: Apply best practices in testing, validation, usability checks, and documentation; research and document data integrity issues using SQL/BI tools.
AI tools & techniques to accelerate insight: Apply AI-assisted approaches (e.g., summarization, insight generation, semantic search, or model-enabled analytics) to reduce time-to-insight and improve stakeholder decision velocity; partner with data science/engineering as needed for productionization paths. (Role expectations align with business analyst + DS/ML collaboration patterns in internal operating guidance.)

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