Requirements

Expected technologies

SAP Data Sphere/SAP BW

Python

SQL

Power BI

Optional technologies

AI

ML

Blockchain

RPA

IoT

Employer requirements

• Master’s degree in data science, business analytics, or similar

• Min. 5 years of experience in data analytics, with at least 3 years in procurement, supply‑chain or finance

• Proven track record turning analytics into savings / value / risk mitigation

• Expert in SAP Data Sphere/SAP BW (or Snowflake), SQL, Python or R

• Advanced Power BI including DAX/M and data-modelling best practice

• Proficient in ML/AI lifecycle & MLOps management

• Agile product management (Scrum / Kanban)

• Interest in emerging technologies like AI, ML, Blockchain, RPA, IoT

• Experience with statistical modelling, forecasting and benefit tracking

• Very good English skills

Your responsibilities

• Design and maintain the central procurement data model in SAP Data Sphere, integrating S/4 HANA, ECC and non-SAP ERPs via ODP, SDI/SDQ and API connectors

• Build, schedule and monitor real-time replication and ETL pipelines, ensuring high-quality, reconciled data for analytics

• Train, validate and retrain models, manage model artefacts and performance dashboards

• Develop interactive Power BI dashboards (DAX/M) visualizing spend trends, ESG metrics and KPI progress

• Publish optimized semantic models from Data Sphere to Power BI; develop advanced DAX measures and composite models

• Ensure data accuracy, refresh reliability and clear business narratives in all reporting assets

• Translate analytical findings into concise recommendations for category managers, finance partners and senior leadership

• Own the analytics backlog: onboard new data sources, KPIs and self-service datasets

• Run agile sprints, demo new features to stakeholders, capture feedback and iterate

• Flag anomalies, propose cleansing rules and collaborate with Data Governance to improve master‑data integrity

• Document metric definitions and calculation logic in the business glossary

• Scout new data sources and analytics techniques

• Share best practices and mentor junior analysts informally

• Perform multidimensional spend‑cube analyses to uncover savings levers, tail‑spend opportunities and compliance gaps, provide ad-hoc analysis for high-value negotiations