What it's about: We are seeking a Data Analyst to play a key role in the data-driven merchandise planning of a leading sporting goods wholesaler. The goal of this position is to actively manage purchasing, category management, and inventory control using modern analytics methods, clear insights, and precise forecasts, with regard to assortment planning and quantity planning. The role focuses on business intelligence, data analysis, and inventory control.
Your tasks: 1. Analytics / Forecasting & Inventory Management
You develop modern demand forecasts (e.g., seasonality and cluster models) to proactively optimize the flow of goods (supply, inventory, NOS).
Trend and what-if analyses provide the basis for strategic assortment and quantity planning.
You analyze large volumes of sales, inventory, and transaction data from ERP, merchandise management, and retail systems.
2. BI Architecture & Insights
You design and automate BI dashboards and KPI systems that provide purchasing and category management with real recommendations for action instead of just raw data.
You transform complex data sets into understandable insights for continuous performance management.
3. Data Foundation
You ensure data integrity in the source systems and build a "Single Source of Truth" for the entire merchandise planning process.
You work closely with IT to detect data anomalies early and to efficiently scale reporting processes.
Our requirements:
Academic background: Completed studies in Data Science, Business Informatics, Statistics, Mathematics, Business Administration or comparable fields.
Professional experience: Several years of experience as a Data Analyst, BI Analyst or in the field of Retail/Wholesale Analytics.
Technical skillset: Solid knowledge of SQL and/or Python.
Analytical strength: Strong skills in quantitative analysis and modeling.
Process understanding: Confident understanding of retail and merchandise flow processes (inventory, forecasting, OOS, goods receipts, sales dynamics).
Communication skills: Ability to process complex data in such a way that it leads to clear recommendations for action for specialist departments.