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Data Analyst / Data Engineer – Process Intelligence & Process Mining
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
Role
The Data Analyst / Data 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 Order-to-Cash (O2C) or Procure-to-Pay (P2P) process. The person will take ownership of transforming complex ERP data into a reliable Celonis process/data model, constructing the required event and analytical structures, and developing PQL-based KPIs and dashboards that reveal process inefficiencies and performance opportunities. This role requires enough technical depth in SQL, relational data structures, ERP data, data transformation, and integration to independently build and 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. Within the first 30–60 days, 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 and 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 and business logic.
2. Build and Validate an End-to-End O2C or P2P Process/Data Model in Celonis. Within the first 60–90 days, 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 and analytical tools may be used to accelerate model development and validation where appropriate.
3. Deliver PQL-Based KPIs and Dashboards That Expose Process Inefficiencies. Within the first 90 days, 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.
4. Establish a Repeatable Foundation for Scaling Process Intelligence Beyond the Initial Process. Over the first 6–12 months, 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. During the first 30 days, develop a working understanding of the assigned O2C or P2P process and the ERP data required to reconstruct it in Celonis. Work with business and technical stakeholders to identify relevant business objects, transactions, tables, keys, timestamps, status changes, and relationships while documenting important assumptions and known data gaps. Direct Epicor or BisTrack experience is valuable but is not required if the person has demonstrated the ability to work effectively with complex ERP environments such as SAP, Oracle, Microsoft Dynamics, or comparable systems. Success will be measured by an accurate process-to-data mapping that provides a reliable blueprint for integration and model development.
2. Establish and Validate the Snowflake-to-Celonis Data Connection. During the first 30–60 days, configure, develop, or support the data integration required to make the relevant ERP-derived Snowflake data reliably available within Celonis. Build and troubleshoot the SQL, extraction, transformation, and loading logic required to support the assigned process while implementing practical controls for completeness, consistency, and refresh reliability. Investigate and resolve data-quality or integration problems that could distort the process model or KPI results. Success will be measured by stable data availability, validated transformations, traceability to source data, and sufficiently reliable refreshes to support ongoing process analysis.
3. Construct the Celonis Process/Data Model and Event Logic. Within the first 60–90 days, transform the connected data into the Celonis structures required to represent the assigned O2C or P2P process accurately. Define cases, activities, timestamps, event sequencing, relationships, and other process attributes while handling complex ERP joins and transformation logic as necessary. Validate the resulting process flows against known business behavior and investigate unexpected variants to distinguish genuine process behavior from data or modeling errors. Success will be measured by model accuracy, technical reliability, stakeholder validation, and the model's ability to support meaningful process exploration.
4. Develop PQL-Based KPIs and Process Analytics. Within the first 90 days, use Celonis PQL and related analytical capabilities to create meaningful KPIs for the assigned process based on agreed business definitions and available data. Develop measures that help expose relevant inefficiencies, bottlenecks, cycle-time issues, rework, exceptions, process variants, or other performance concerns rather than simply reporting available fields. Validate KPI calculations against underlying source data and business expectations before presenting conclusions. Success will be measured by KPI accuracy, relevance, traceability, and stakeholder confidence in using the measures to evaluate process performance.
5. Create Action-Oriented Dashboards and Communicate Process Findings. Within the first 90 days and iteratively thereafter, create Celonis dashboards and visual analytics that enable business and technical stakeholders to understand actual process performance and investigate identified inefficiencies. Structure dashboards around meaningful process questions and KPIs rather than simply presenting large volumes of data, and communicate findings in clear business terms supported by evidence. Work collaboratively with stakeholders to refine views as understanding of the process develops. Success will be measured by dashboard usability, stakeholder adoption, analytical clarity, and the ability to identify and communicate specific areas requiring further investigation or improvement.
6. Document and Standardize the Process Intelligence Solution. Throughout the initial implementation and first year, maintain clear documentation covering source systems, Snowflake data structures, SQL and transformation logic, data lineage, Celonis process/data models, event definitions, PQL calculations, KPI definitions, dashboards, assumptions, and known limitations. Coordinate with the engineer responsible for the complementary O2C or P2P process to identify reusable patterns and establish practical consistency across implementations. Success will be measured by documentation completeness, maintainability, reuse of validated assets, and the ability for other qualified team members to understand and extend the solution without excessive dependence on individual knowledge.
7. Continuously Evaluate and Integrate AI to Improve Performance. Within the first 90–180 days, take ownership of identifying how AI and automation can improve the speed, quality, and scalability of process-intelligence delivery. Evaluate appropriate uses of AI to support SQL development, ERP data mapping, transformation logic, data-quality analysis, PQL development, documentation, process-pattern discovery, root-cause exploration, and analytical workflows while maintaining appropriate human validation and data governance. Pilot high-value use cases where AI can measurably reduce manual effort or accelerate the path from raw ERP data to validated process insights, documenting successful methods for broader reuse. Success will be measured by demonstrated improvements in analytical productivity, solution quality, or delivery speed and by the responsible integration of effective AI-enabled practices into the process-mining workflow.
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
Role
The Data Analyst / Data 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 Order-to-Cash (O2C) or Procure-to-Pay (P2P) process. The person will take ownership of transforming complex ERP data into a reliable Celonis process/data model, constructing the required event and analytical structures, and developing PQL-based KPIs and dashboards that reveal process inefficiencies and performance opportunities. This role requires enough technical depth in SQL, relational data structures, ERP data, data transformation, and integration to independently build and 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. Within the first 30–60 days, 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 and 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 and business logic.
2. Build and Validate an End-to-End O2C or P2P Process/Data Model in Celonis. Within the first 60–90 days, 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 and analytical tools may be used to accelerate model development and validation where appropriate.
3. Deliver PQL-Based KPIs and Dashboards That Expose Process Inefficiencies. Within the first 90 days, 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.
4. Establish a Repeatable Foundation for Scaling Process Intelligence Beyond the Initial Process. Over the first 6–12 months, 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. During the first 30 days, develop a working understanding of the assigned O2C or P2P process and the ERP data required to reconstruct it in Celonis. Work with business and technical stakeholders to identify relevant business objects, transactions, tables, keys, timestamps, status changes, and relationships while documenting important assumptions and known data gaps. Direct Epicor or BisTrack experience is valuable but is not required if the person has demonstrated the ability to work effectively with complex ERP environments such as SAP, Oracle, Microsoft Dynamics, or comparable systems. Success will be measured by an accurate process-to-data mapping that provides a reliable blueprint for integration and model development.
2. Establish and Validate the Snowflake-to-Celonis Data Connection. During the first 30–60 days, configure, develop, or support the data integration required to make the relevant ERP-derived Snowflake data reliably available within Celonis. Build and troubleshoot the SQL, extraction, transformation, and loading logic required to support the assigned process while implementing practical controls for completeness, consistency, and refresh reliability. Investigate and resolve data-quality or integration problems that could distort the process model or KPI results. Success will be measured by stable data availability, validated transformations, traceability to source data, and sufficiently reliable refreshes to support ongoing process analysis.
3. Construct the Celonis Process/Data Model and Event Logic. Within the first 60–90 days, transform the connected data into the Celonis structures required to represent the assigned O2C or P2P process accurately. Define cases, activities, timestamps, event sequencing, relationships, and other process attributes while handling complex ERP joins and transformation logic as necessary. Validate the resulting process flows against known business behavior and investigate unexpected variants to distinguish genuine process behavior from data or modeling errors. Success will be measured by model accuracy, technical reliability, stakeholder validation, and the model's ability to support meaningful process exploration.
4. Develop PQL-Based KPIs and Process Analytics. Within the first 90 days, use Celonis PQL and related analytical capabilities to create meaningful KPIs for the assigned process based on agreed business definitions and available data. Develop measures that help expose relevant inefficiencies, bottlenecks, cycle-time issues, rework, exceptions, process variants, or other performance concerns rather than simply reporting available fields. Validate KPI calculations against underlying source data and business expectations before presenting conclusions. Success will be measured by KPI accuracy, relevance, traceability, and stakeholder confidence in using the measures to evaluate process performance.
5. Create Action-Oriented Dashboards and Communicate Process Findings. Within the first 90 days and iteratively thereafter, create Celonis dashboards and visual analytics that enable business and technical stakeholders to understand actual process performance and investigate identified inefficiencies. Structure dashboards around meaningful process questions and KPIs rather than simply presenting large volumes of data, and communicate findings in clear business terms supported by evidence. Work collaboratively with stakeholders to refine views as understanding of the process develops. Success will be measured by dashboard usability, stakeholder adoption, analytical clarity, and the ability to identify and communicate specific areas requiring further investigation or improvement.
6. Document and Standardize the Process Intelligence Solution. Throughout the initial implementation and first year, maintain clear documentation covering source systems, Snowflake data structures, SQL and transformation logic, data lineage, Celonis process/data models, event definitions, PQL calculations, KPI definitions, dashboards, assumptions, and known limitations. Coordinate with the engineer responsible for the complementary O2C or P2P process to identify reusable patterns and establish practical consistency across implementations. Success will be measured by documentation completeness, maintainability, reuse of validated assets, and the ability for other qualified team members to understand and extend the solution without excessive dependence on individual knowledge.
7. Continuously Evaluate and Integrate AI to Improve Performance. Within the first 90–180 days, take ownership of identifying how AI and automation can improve the speed, quality, and scalability of process-intelligence delivery. Evaluate appropriate uses of AI to support SQL development, ERP data mapping, transformation logic, data-quality analysis, PQL development, documentation, process-pattern discovery, root-cause exploration, and analytical workflows while maintaining appropriate human validation and data governance. Pilot high-value use cases where AI can measurably reduce manual effort or accelerate the path from raw ERP data to validated process insights, documenting successful methods for broader reuse. Success will be measured by demonstrated improvements in analytical productivity, solution quality, or delivery speed and by the responsible integration of effective AI-enabled practices into the process-mining workflow.