Process Mining Technical Analyst/Consultant

Miebach Business Consulting and Services•Fishers, IN

3d•Hybrid

About The Position

We are seeking a Process Mining Technical Analyst to provide technical support throughout process mining projects. The role focuses on data preparation, event-log development, process mining configuration, data validation, troubleshooting, and analytical support. The successful candidate must understand process and material flows, particularly within warehouse, transportation, manufacturing, or supply chain environments. They will work closely with process mining consultants, data engineers, project managers, and client teams to ensure that the required data, process logic, KPIs, and dashboards are correctly developed and validated. The role may contribute to identifying process issues and improvement opportunities, but developing business recommendations is not the primary responsibility.

Requirements

Bachelor’s degree in Industrial Engineering, Supply Chain, Data Analytics, Information Systems, Computer Science, or a related field
2–3 years of experience in data analysis, business intelligence, process mining, systems analysis, or technical consulting
Strong understanding of process flows and material flows
Experience working with transactional or operational data
Practical experience with SQL and data preparation
Strong attention to detail and ability to validate complex data relationships
Ability to understand system transactions and connect them to real operational activities
Strong troubleshooting and problem-solving skills
Good written and verbal communication skills
Ability to work effectively with both technical and business stakeholders
Strong SQL skills, including joins, aggregations, window functions, and data-quality checks
Understanding of data modeling, relational databases, and ETL/ELT concepts
Strong Excel skills for data analysis, validation, and reconciliation
Ability to understand data structures and connect system transactions to operational and material flows

Nice To Haves

Experience with PQL, particularly when working with Celonis
Familiarity with process-mining platforms such as Celonis, SAP Signavio Process Intelligence, UiPath Process Mining, IBM Process Mining, or Apromore is preferred; experience with one or more platforms is sufficient
Experience with Power BI or Tableau is preferred
Familiarity with AI, machine learning, or advanced analytics in process-mining work—such as anomaly detection, root-cause analysis, or predictive insights—is a plus
Exposure to APIs, cloud platforms, or data lakes is an asset

Responsibilities

Work collaboratively as an active member of multidisciplinary project teams.
Support process mining projects from data discovery through delivery and handover.
Participate in project meetings, workshops, working sessions, and client reviews.
Provide regular updates on progress, risks, dependencies, and open technical topics.
Understand and document operational processes and material flows.
Translate business and process requirements into technical data requirements.
Identify process objects, activities, timestamps, attributes, and relationships.
Support data mapping between source systems and the process mining platform.
Extract, clean, transform, and prepare data from WMS, TMS, ERP, MES, YMS, and other systems.
Build and maintain event logs and process mining data models.
Validate data, process logic, calculations, and analytical outputs.
Investigate data-quality issues, missing events, inconsistent master data, and unexpected results.
Configure process mining analyses, KPIs, dashboards, filters, and process views.
Contribute to the identification of process deviations, bottlenecks, and improvement opportunities when required.
Support the preparation of project deliverables, demonstrations, and client presentations.
Troubleshoot technical issues and coordinate resolution with the project team, IT teams, and platform specialists.
Prepare test cases and support system testing and user acceptance testing.
Assist with validating system functionality, data connections, and analytical outputs.
Maintain technical documentation, data dictionaries, assumptions logs, and configuration records.
Document data sources, transformations, process logic, testing results, and relevant project decisions.