WMS Data Analyst

BE PART OF THE WORLD’S LARGEST LOGISTICS COMPANY

Deutsche Post DHL Group is the world’s leading logistics and mail company. We are one of the world’s largest employers, operating in over 220 countries and territories. We are Europe’s largest postal service, partner for eCommerce and pioneers in secure digital communication. We are number one in contract logistics and international express delivery, and a leader in the forwarding business. Join us and you will be working for a global company that is focused on service, quality, and sustainability, and using the power of global trade to connect people and improve lives.

And not just for our customers, but for every member of our Group too.

At DHL Supply Chain, we are looking for a data driven and solution-oriented...

To enable data driven warehouse operations across multiple sites by extracting, modelling, and analysing data from Manhattan WMS (WMOS
2017 and Manhattan Active) and related systems to enhance productivity, accuracy, and service levels. Develop reliable reporting assets and translate operational challenges into analytical insights that drive measurable and sustainable outcomes.

• 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.

• 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.

• 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.

• 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/Git Flow.
• 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.

• Stakeholder management.
• Identifying and analysing information to understand project requirements, key data, and decision drivers.
• Applying technical skills to create and model scenarios for current and future…