Analyst Data Analysis Jobs in Romeoville, IL (NOW HIRING)

Position: Data Analyst / Data Engineer - Process Intelligence & Process Mining
Primary Platform: Celonis
Data Environment: Snowflake and Enterprise ERP Systems
Initial Process Scope: Order-to-Cash (O2C) or Procure-to-Pay (P2P)
Employment Type: Full-Time, W2, Chicago Preferred, Hybrid
We are seeking a Data Analyst / Data Engineer to help build our Process Intelligence capability from the ground up using Celonis, Snowflake, and enterprise ERP data. Initially focused on either the Order-to-Cash (O2C) or Procure-to-Pay (P2P) process, this role will own the transformation of ERP data into scalable Celonis process models, develop meaningful PQL-based analytics, and provide actionable insights that improve business performance.
This is a hands-on implementation role. We are looking for someone who can become productive with limited ramp-up time and begin contributing almost immediately. The expectation is not that the individual is a senior Celonis expert; however, they should possess enough practical, project-based experience to independently perform common platform tasks, troubleshoot basic issues, and participate in solution delivery without requiring extensive foundational training. Candidates with approximately six months of genuine hands-on Celonis project experience who understand how to navigate the platform, work with process and data models, and build basic analytics should be capable of succeeding in this role.
Success requires strong SQL and data engineering skills, an understanding of ERP data structures, practical experience with process mining, and the ability to translate technical analysis into business process improvements while collaborating effectively with both technical and business stakeholders.
Proposition
This role offers the opportunity to build a new Process Intelligence capability rather than simply maintain an existing analytics environment. Your work will directly influence how the organization understands and improves critical business processes by transforming operational ERP data into actionable process insights.
You will deepen your expertise across Celonis, Snowflake, ERP data architecture, SQL, PQL, process mining, process modeling, and business process optimization while helping establish standards that will support future process intelligence initiatives across the organization.
This opportunity is ideal for someone who enjoys solving complex data challenges, building analytical solutions from the ground up, and helping organizations uncover opportunities to improve operational performance through process mining.
Performance Objectives
1. Establish the Snowflake-to-Celonis Analytical Foundation
Within the first 30-60 days, establish and validate the data connection between Snowflake and Celonis for the assigned O2C or P2P process. Develop the SQL, transformations, and source-to-target mappings necessary to create a reliable analytical foundation while validating data quality and resolving integration issues. Success will be measured by a stable, repeatable data pipeline, validated transformation logic, and stakeholder confidence in the integrity of the underlying data. AI-assisted SQL development, data profiling, and documentation tools may be used where appropriate while ensuring all outputs are validated against business rules.
2. Build a Reliable End-to-End Process Model
Within the first 60-90 days, develop a validated Celonis process and data model that accurately represents the assigned business process. Construct event logs, define case structures, activities, timestamps, and relationships, and resolve complex ERP data challenges to ensure the model accurately reflects real-world process execution. Success will be measured through stakeholder validation, model accuracy, technical reliability, and support for meaningful process analysis.
3. Deliver Actionable Process Intelligence
Within the first 90 days, develop PQL-based KPIs, dashboards, and analytical views that identify bottlenecks, rework, compliance issues, process variants, and other opportunities for operational improvement. Translate technical findings into business-focused insights that enable stakeholders to prioritize improvement initiatives. Success will be measured through dashboard adoption, KPI accuracy, stakeholder acceptance, and the identification of measurable process improvement opportunities.
4. Build a Scalable Process Intelligence Capability
During the first 6-12 months, establish reusable data models, PQL logic, documentation standards, and implementation practices that support future Process Intelligence initiatives beyond the initial O2C or P2P deployment. Collaborate with peers to ensure consistency across implementations while reducing future development effort. Success will be measured through reusable assets, standardized practices, improved implementation efficiency, and reduced dependency on individual knowledge.
5. Become Productive Quickly with Minimal Ramp-Up
Within the first 30 days, demonstrate the ability to independently navigate the Celonis platform, understand the assigned business process, and contribute to project deliverables with minimal supervision. The successful candidate should be capable of performing common platform activities, supporting data-model development, modifying analytical objects, and troubleshooting basic issues without requiring extensive platform training. Success will be measured by the ability to contribute meaningful work early in the engagement while demonstrating technical competence, sound judgment, and increasing ownership of assigned responsibilities.
Critical Subtasks
1. Understand the Assigned Business Process and ERP Data
Develop a working understanding of the assigned O2C or P2P process by identifying the business objects, transactions, timestamps, relationships, and ERP data structures required to reconstruct the process in Celonis. Validate assumptions with business stakeholders and document known data gaps to establish a reliable foundation for process modeling.
2. Build and Validate the Snowflake-to-Celonis Integration
Develop and troubleshoot the SQL, transformation logic, and integration processes required to move ERP data from Snowflake into Celonis. Validate data quality, resolve integration issues, and ensure reliable refreshes that support ongoing process analysis.
3. Construct the Celonis Process and Data Model
Develop the event log, case definitions, activities, relationships, and supporting process structures required to accurately model the assigned business process. Validate process flows against business expectations and investigate unexpected process variants to distinguish genuine operational behavior from data or modeling issues.
4. Develop PQL-Based KPIs and Analytics
Create meaningful KPIs using Celonis PQL that expose bottlenecks, delays, rework, exceptions, compliance concerns, and other operational performance indicators. Validate calculations against source data before communicating analytical findings to stakeholders.
5. Create Business-Focused Dashboards and Process Insights
Develop dashboards that enable stakeholders to understand process performance and identify opportunities for improvement. Present analytical findings in clear business language and refine reporting based on stakeholder feedback and evolving business needs.
6. Document and Standardize the Solution
Maintain comprehensive documentation covering source systems, SQL transformations, data lineage, process models, KPI definitions, dashboards, assumptions, and implementation standards to support future scalability and knowledge transfer across the Process Intelligence team.
7. Continuously Evaluate and Integrate AI to Improve Performance
Within the first 90-180 days, identify opportunities to leverage AI and automation to improve SQL development, ERP data mapping, PQL creation, documentation, data-quality analysis, and process discovery. Pilot AI-enabled approaches that improve productivity while maintaining appropriate governance and human validation. Success will be measured by demonstrable improvements in delivery speed, analytical quality, and the responsible adoption of AI-supported practices.