Role purpose
Maintains and enhances the data pipelines, transformations, data models, monitoring controls and releases that support the live warehouse solution and, once released, the transportation control tower.
Key responsibilities
- Monitor scheduled Celonis data jobs, extractions, transformations and refresh cycles
- Investigate and resolve failed loads, incomplete refreshes, reconciliation issues and performance degradation
- Maintain data connections to SAP ECC, logistics data repositories, Databricks, a TMS and related sources
- Support Celonis data models, activity tables, case tables, object tables and KPI calculations
- Troubleshoot data quality issues such as missing milestones, duplicates, timestamp inconsistencies and key mapping failures
- Maintain incremental loading and refresh mechanisms
- Perform root cause analysis for production data defects
- Implement approved data fixes, minor enhancements and configuration changes
- Maintain monitoring, reconciliation, audit, lineage and recovery controls
- Support testing, release preparation, deployment and post release validation
- Document transformations, data mappings, dependencies and operating procedures
- Take part in shift handovers and own engineering issues during the assigned shift
- Support the onboarding of new control tower capabilities into sustainment
Required skills and experience
- 6+ years of data engineering, process mining, BI or enterprise data platform experience
- Hands on experience with Celonis Data Integration and Celonis data modelling
- Strong SQL and data transformation skills
- Experience with SAP ECC logistics data: orders, deliveries, material movements, warehouse or transportation records
- Familiarity with SAP extraction, APIs, JDBC connections, SFTP and incremental loading
- Experience with data reconciliation, defect analysis, performance tuning and release management
Preferred
- Celonis PQL
- Case centric process mining
- Object centric process mining
- SAP S/4HANA exposure
- Celonis Data Engineer certification
Success measures
- Timely and successful data refreshes
- Accurate, reconciled business data
- Fewer recurring job and transformation failures
- Controlled deployment of data model changes
- Complete technical documentation and runbooks