Data Analyst/Engineer

Data Analyst/Engineer – Process Intelligence & Process Mining Platform: Celonis Environment: Snowflake & Enterprise ERP Systems Scope: Order-to-Cash (O2C) & Procure-to-Pay (P2P) Full-Time, W2, hybrid from Chicago The Data Analyst/Engineer – Process Intelligence & Process Mining will help build a new process-intelligence capability from the ground up, with an initial focus on connecting enterprise ERP data through Snowflake into Celonis and creating actionable visibility into either the O2C or P2P process. The person will take ownership of transforming complex ERP data into a reliable Celonis process/data model, constructing the required event & analytical structures, and developing PQL-based KPIs & dashboards that reveal process inefficiencies & performance opportunities. This role requires enough technical depth in SQL, relational data structures, ERP data, data transformation, and integration to independently build & troubleshoot the analytical foundation supporting process mining. Equally important, the person must have practical process-mining experience and be able to translate technical data into meaningful process insights rather than simply building pipelines or reports. The successful candidate will work collaboratively with business and technical stakeholders as the organization establishes this capability from scratch, with another engineer expected to support the complementary O2C or P2P process. Over time, the work should establish a repeatable foundation for expanding process intelligence into additional processes and use cases. Purpose: This role offers the opportunity to build a process-intelligence capability from its earliest stage rather than simply maintain an established analytics environment. The person will directly connect enterprise ERP data to Celonis and use process mining to reveal how critical O2C or P2P processes operate, where inefficiencies exist, and where the business should focus improvement efforts. Growth: The successful candidate will deepen expertise across Celonis, Snowflake, ERP data, process mining, PQL, process/data modeling, and business-process analytics while helping establish a new capability from scratch. The role provides the opportunity to move beyond dashboard development into end-to-end ownership of how raw operational data becomes a process model, how performance is measured, and how process insights are communicated and scaled. Motivators: This opportunity is particularly suited to someone who enjoys taking an ambiguous process problem and building the analytical solution needed to make it visible and measurable. The person will have meaningful ownership—from connecting and transforming source data through developing the Celonis model and ultimately creating KPIs and dashboards that expose real process inefficiencies and improvement opportunities. Objectives 1. Establish the Snowflake-to-Celonis Data Connection and Analytical Foundation. Establish and validate the data connection required to move the relevant ERP-derived data from Snowflake into Celonis for the assigned O2C or P2P process. Understand the underlying ERP data structures, identify the transactions, business objects, timestamps, identifiers, and relationships required for process analysis, and develop the SQL, joins, transformations, and supporting data logic necessary to create a reliable analytical foundation. Validate data completeness and accuracy with appropriate business & technical stakeholders and resolve material data-quality or integration issues that could compromise downstream analysis. Success will be demonstrated by a stable and repeatable Snowflake-to-Celonis data connection, documented transformation logic, validated source-to-target mappings, and reliable data available for process-model development. AI-assisted SQL development, data profiling, documentation, and troubleshooting tools may be used where appropriate, with all outputs validated against source data & business logic. 1. Build and Validate an End-to-End O2C or P2P Process/Data Model in Celonis. Build a functioning Celonis process/data model for the assigned Order-to-Cash or Procure-to-Pay process using the connected ERP data. Develop the necessary event-log structures, case definitions, activities, timestamps, relationships, transformations, and analytical logic required to reconstruct how the process executes. Use advanced SQL and Celonis capabilities to address complex ERP joins, event-log construction, data modeling, transformations, troubleshooting, and performance issues as required. Success will be demonstrated by a validated Celonis model that accurately represents the assigned business process, supports reliable process exploration and KPI calculations, and is accepted by relevant stakeholders as a trustworthy representation of actual process execution. AI-enabled development & analytical tools may be used to accelerate model development & validation where appropriate. 1. Deliver PQL-Based KPIs & Dashboards That Expose Process Inefficiencies. Create PQL-based KPIs, dashboards, and analytical views within Celonis that allow stakeholders to identify and monitor inefficiencies in the assigned O2C or P2P process. Analyze the process for bottlenecks, delays, rework, undesirable variants, exceptions, compliance deviations, and other meaningful performance issues supported by the available data. Translate complex process-mining findings into clear visual analytics that enable business and technical stakeholders to understand where performance is breaking down and where deeper investigation or improvement should be considered. Success will be demonstrated by validated KPI definitions, functioning dashboards, stakeholder acceptance of the analysis, and the ability to use the solution to identify specific evidence-based process inefficiencies and establish baseline performance measures. 1. Establish a Repeatable Foundation for Scaling Process Intelligence Beyond the Initial Process. Convert the initial O2C or P2P implementation into reusable methods, data structures, PQL logic, documentation, and analytical practices that can support the complementary process and future process-mining initiatives. Collaborate with the engineer responsible for the other initial process to promote consistency in data integration, modeling, KPI definitions, documentation, and solution design while recognizing legitimate differences between O2C and P2P. Identify lessons learned and technical or analytical patterns that can reduce implementation effort and improve quality as additional processes are introduced. Success will be demonstrated by reusable assets, documented standards, consistent analytical practices, and a scalable foundation that reduces dependence on one-off development. Subtasks 1. Understand the Assigned Business Process and Map the ERP Data. 1. Establish and Validate the Snowflake-to-Celonis Data Connection. 1. Construct the Celonis Process/Data Model and Event Logic. 1. Develop PQL-Based KPIs and Process Analytics. 1. Create Action-Oriented Dashboards and Communicate Process Findings. 1. Document and Standardize the Process Intelligence Solution. 1. Continuously Evaluate and Integrate AI to Improve Performance.