Process Intelligence Engineer

The Process Intelligence Engineer is responsible for designing, building, and maintaining Industrial & Systems Engineering data modeling and analytics solutions that support data‑driven decision‑making across logistics, supply chain, and manufacturing operations.

The 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‑focused initiatives.

Key Responsibilities

Decision Intelligence & Analytics

• 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.
  

Simulation & Decision Modeling Skills

• Applies statistical and scenario‑based simulation techniques to evaluate business outcomes, operational tradeoffs, and decision alternatives.

• Uses what‑if analysis, Monte Carlo simulation, sensitivity analysis, and probabilistic modeling to assess risk, variability, and performance impacts across key metrics.

• Supports capacity, demand, throughput, and service‑level analysis using historical data and modeled assumptions rather than detailed process‑engineering tools.

• Partners with engineering, operations, and analytics teams to frame simulation inputs, assumptions, and constraints aligned with real‑world execution.

• Communicates simulation results clearly through dashboards, visualizations, and narratives to support leadership decision‑making.

• Leverages Python, SQL, and analytical tooling to build lightweight, repeatable simulation models that integrate with BI datasets and reporting workflows.
  

Education & Experience

• 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.
  

Preferred Skills

• 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.