About the role

AI summarised

The Process Intelligence Engineer designs and maintains data analytics solutions to support data-driven decision-making in logistics, supply chain, and manufacturing. They build dashboards, ETL pipelines, and analytical models using tools like Power BI, Tableau, SQL, and Python, while collaborating with cross-functional teams to deliver actionable insights. The role also involves simulation, predictive modeling, and guiding junior analysts in best practices for data visualization and process improvement.

EquipmentOnsiteBusiness 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
Partner 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
Solve complex data, visualization, and reporting problems using structured analysis, KPI decomposition, and data validation methodologies

Requirements

Bachelor’s degree required; Master’s preferred in Industrial & Systems Engineering, Computer Science, Business Analytics, Systems Engineering or a related field
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 of 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
Serves as a resource for junior analysts; may lead smaller BI projects or reporting workstreams
Works with business and engineering stakeholders to drive alignment on metric definitions and reporting standards