About the role

AI summarised

The Process Intelligence Engineer at Applied Materials designs and maintains data modeling and analytics solutions to support data-driven decision-making across logistics, supply chain, and manufacturing operations. The role involves developing dashboards, ETL pipelines, and analytical models using tools like Power BI, Tableau, SQL, and Python, and collaborating with cross-functional teams to drive operational visibility and continuous improvement.

EquipmentFull-timeBusiness Operations

Key Responsibilities

Work in cross‑functional teams to design and develop reporting solutions enabling data‑driven decisions for logistics, supply chain, and manufacturing teams.
Partners with GIS, Engineering, and Operations teams to align process analytics initiatives with broader analytics and automation efforts.
Develop and maintain dashboards, analytical data models, and KPI frameworks using Power BI, Tableau, or equivalent BI platforms.
Build scalable ETL data pipelines for ingestion, cleansing, integration, and transformation of large datasets across SAP, Databricks, SQL data warehouses, and operational systems.
Perform ad‑hoc statistical, diagnostic, and root‑cause analysis using SQL and Python to support business investigations.
Interface with internal customers for requirements gathering; translate business problems into reporting specifications and analytical outputs.
Create automated workflows to ensure timely refresh and reliability of datasets, dashboards, and scorecards.
Generate reports, technical documentation, business presentations, and stakeholder communications for operations and leadership.
Continuously evaluate visualization, and reporting technologies; recommend improvements for reporting efficiency, data quality, and automation.
Provide guidance to team members on best data and process practices, visualization standards, metric definitions, and structured problem‑solving approaches.
Enable descriptive to predictive modeling and predictive to prescriptive modeling using standard datasets to optimize warehousing.
Applies statistical and scenario‑based simulation techniques to evaluate business outcomes, operational tradeoffs, and decision alternatives.

Requirements

Education: Bachelor's degree required; Master's preferred in Industrial & Systems Engineering, Computer Science, Business Analytics, Systems Engineering or a related field.
Experience: 4–7 years of experience in process intelligence, analytics, dashboarding, or data‑engineering–adjacent environments.
Strong proficiency in SQL and Python for analytics and problem‑solving.
Expertise in Power BI, Tableau, or equivalent visualization tools.
Experience with cloud and big‑data platforms (Azure, Databricks, Snowflake, AWS, GCP).
Knowledge of ETL/ELT frameworks, data modeling techniques (star/snowflake schemas), and DAX or similar analytical expressions.
Understanding Data automation, data refresh pipelines, and reporting governance.
Strong communication, stakeholder management, and collaboration skills.
Curious, analytical mindset with interest in operational analytics and continuous improvement.
Strong conceptual and hands‑on expertise in data modeling, dashboard design, KPI definitions, process intelligence and business analytics.
Working knowledge of engineering workflows, cloud data platforms, and SQL‑based data warehouse systems.
Familiarity with supply chain, logistics, planning, or manufacturing analytics is a plus.
Understands how BI integrates with operations, logistics, supply chain, and enterprise analytics ecosystems.
Familiar with best practices in reporting governance, master data alignment, and BI lifecycle management.