Process Intelligence Engineer

Role Overview\n\nThe Process Intelligence Engineer is responsible for designing, building, and maintaining Industrial \u0026 Systems Engineering data modeling and analytics solutions that support data\u2011driven decision\u2011making across logistics, supply chain, and manufacturing operations.\n\nThe role works closely with engineering and operations stakeholders to translate business questions into scalable reporting, analytical models, and actionable insights, supporting operational visibility, execution tracking, and continuous improvement across domain\u2011focused initiatives.\n\nKey Responsibilities\n\nDecision Intelligence \u0026 Analytics\n\n * Work in cross\u2011functional teams to design and develop reporting solutions enabling data\u2011driven decisions for logistics, supply chain, and manufacturing teams.\n * Partners with GIS, Engineering, and Operations teams to align process analytics initiatives with broader analytics and automation efforts.\n * Develop and maintain dashboards, analytical data models, and KPI frameworks using Power BI, Tableau, or equivalent BI platforms.\n * Build scalable ETL data pipelines for ingestion, cleansing, integration, and transformation of large datasets across SAP, Databricks, SQL data warehouses, and operational systems.\n * Perform ad\u2011hoc statistical, diagnostic, and root\u2011cause analysis using SQL and Python to support business investigations.\n * Interface with internal customers for requirements gathering; translate business problems into reporting specifications and analytical outputs.\n * Create automated workflows to ensure timely refresh and reliability of datasets, dashboards, and scorecards.\n * Generate reports, technical documentation, business presentations, and stakeholder communications for operations and leadership.\n * Continuously evaluate visualization, and reporting technologies; recommend improvements for reporting efficiency, data quality, and automation.\n * Provide guidance to team members on best data and process practices, visualization standards, metric definitions, and structured problem\u2011solving approaches.\n * Enable descriptive to predictive modeling and predictive to prescriptive modeling using standard datasets to optimize warehousing.\n\n\n\nFunctional Knowledge\n\n * Strong conceptual and hands\u2011on expertise in data modeling, dashboard design, KPI definitions, process intelligence and business analytics.\n * Working knowledge of engineering workflows, cloud data platforms, and SQL\u2011based data warehouse systems.\n * Familiarity with supply chain, logistics, planning, or manufacturing analytics is a plus.\n\n\n\nBusiness Expertise\n\n * Understands how BI integrates with operations, logistics, supply chain, and enterprise analytics ecosystems.\n * Familiar with best practices in reporting governance, master data alignment, and BI lifecycle management.\n\n\n\nLeadership\n\n * Serves as a resource for junior analysts may lead smaller BI projects or reporting workstreams.\n * Works with business and engineering stakeholders to drive alignment on metric definitions and reporting standards.\n\n\n\nProblem Solving\n\n * Solves complex data, visualization, and reporting problems using structured analysis, KPI decomposition, and data validation methodologies.\n * Independently identifies issues in data quality, metric inconsistencies, and reporting gaps.\n\n\n\nSimulation \u0026 Decision Modeling Skills\n\n * Applies statistical and scenario\u2011based simulation techniques to evaluate business outcomes, operational tradeoffs, and decision alternatives.\n * Uses what\u2011if analysis, Monte Carlo simulation, sensitivity analysis, and probabilistic modeling to assess risk, variability, and performance impacts across key metrics.\n * Supports capacity, demand, throughput, and service\u2011level analysis using historical data and modeled assumptions rather than detailed process\u2011engineering tools.\n * Partners with engineering, operations, and analytics teams to frame simulation inputs, assumptions, and constraints aligned with real\u2011world execution.\n * Communicates simulation results clearly through dashboards, visualizations, and narratives to support leadership decision\u2011making.\n * Leverages Python, SQL, and analytical tooling to build lightweight, repeatable simulation models that integrate with BI datasets and reporting workflows.\n\n\n\nImpact\n\n * Directly impacts operational visibility, decision intelligence, and reporting accuracy across multiple teams.\n * Ensures timely availability of high\u2011quality dashboards and insights, influencing performance and execution.\n\n\n\nInterpersonal Skills\n\n * Clearly explains analytical findings and process intelligence outputs to cross\u2011functional leaders.\n * Builds consensus on metric definitions and visualization standards across teams.\n\n\n\nEducation \u0026 Experience\n\n * Education: Bachelor\u2019s degree required; Master\u2019s preferred in Industrial \u0026 Systems Engineering, Computer Science, Business Analytics, Systems Engineering or a related field.\n * Experience: 4\u20137 years of experience in process intelligence, analytics, dashboarding, or data\u2011engineering\u2013adjacent environments.\n\n\n\nPreferred Skills\n\n * Strong proficiency in SQL and Python for analytics and problem\u2011solving.\n * Expertise in Power BI, Tableau, or equivalent visualization tools.\n * Experience with cloud and big\u2011data platforms (Azure, Databricks, Snowflake, AWS, GCP).\n * Knowledge of ETL/ELT frameworks, data modeling techniques (star/snowflake schemas), and DAX or similar analytical expressions.\n * Understanding Data automation, data refresh pipelines, and reporting governance.\n * Strong communication, stakeholder management, and collaboration skills.\n * Curious, analytical mindset with interest in operational analytics and continuous improvement.\n\n\n\n## Qualifications\n\n### Education:\n\nBachelor\u0027s Degree\n\n### Skills\n\n### Certifications:\n\n### Languages:\n\n### Years of Experience:\n\n4 - 7 Years\n\n### Work Experience:\n\n## Additional Information\n\n### \n\n### Shift:\n\nDay (Singapore)\n\n### \n\n### Travel:\n\n### \n\n### Relocation Eligible:\n\nNo\n\n### Referral Payment Plan:\n\nEmployee Referral (Enhanced)\n\nApplied Materials is an Equal Opportunity Employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law. \n