WMS Data Analyst at DHL Supply Chain

Key Tasks

• Design and maintain Power BI dashboards and data models for inbound, outbound, replenishment, inventory control, cycle counting, and labour performance. 
• Develop KPI definitions, calculation logic, and data lineage for a single source of truth across sites. 
• Perform root-cause analysis on short/mis-picks, wave delays, congestion, and queue bottlenecks; recommend process changes. 
• Extract and transform data from Manhattan WMOS / Manhattan Active WMS (including order streaming, allocation, replenishment, carbonisation, slotting, RF flows). 
• Implement reliable ELT/ETL from WMS and adjacent systems (TOMS/TMS/Other sources) into the data warehouse/lake with robust error handling and reconciliation. 
• Define and monitor data quality rules (completeness, validity, consistency); maintain a data dictionary and steward reference/master data. 
• Partner with Operations, Site Leads, Solutions Design, Engineering, and Finance to prioritize analytics use cases and land insights into SOPs. 
• Support UAT, go-live, and hyper care for analytics components; align with ITIL incident/problem/change processes. 
• Coach super users; drive adoption and ensure role-based access controls and compliance (e.g., POPIA). 
• Adhere to company Information Security policies, POPIA, and role-based access controls. 
• Ensure KPI and data lineage documentation is complete and audit ready.

Measurement Criteria / KPIs

• Pick accuracy ≥ 99.8%; inventory accuracy ≥ 99.9%. 
• Order cycle time reduced by ≥ 10% at targeted sites. 
• Data quality score ≥ 98% on critical WMS data elements. 
• Standard dashboards deployed at 100% of target sites with ≥ 85% monthly active usage. 
• BAU data load success rate ≥ 99.5%; P1 incident TRT ≤ 4 hours.

Qualifications & Experience

Education:

• Bachelor’s degree in Supply Chain, Industrial Engineering, Information Systems, or a related field (or equivalent experience). 
• Proficiency in SQL (Oracle preferred), Power BI (DAX, M), and dimensional data modelling. 
• Certifications in Lean/Six Sigma (Yellow/Green Belt) and ITIL Foundation.

Experience:

• 4–6+ years of warehouse/WMS analytics experience, with ≥3 years hands-on experience in Manhattan WMS (WMOS or Active). 
• Experience with Manhattan SCI, Labor Management (LMS), DOM, Slotting, or Yard modules. 
• Experience with Azure (ADF, Synapse/SQL, Databricks), Python for analytics, and Git/GitFlow. 
• Strong understanding of DC processes, including receiving, picking, packing, shipping, inventory control, and RF workflows. 
• Experience with waving strategies, picking methods (cluster, batch, zone), replenishment, and cartonisation. 
• Skilled in building incremental data pipelines (CDC/timestamped loads), orchestration, and error handling. 
• Knowledge of data governance, KPI auditability, and role-based security (RLS) in Power BI.