Data Analyst (m/f/d)
eitrawmaterialsgmbh
| Company | eitrawmaterialsgmbh |
| Category | Data & Analytics |
| Location | — |
| Remote | — |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 24 Apr 2026 |
| Last verified | 3 Aug 2026 |
| Source | Employer ATS (teamtailor) |
Description
Company Description About EIT RawMaterials As the world moves towards a greener future, the demand for raw materials—minerals and metals—is set to increase immensely over the next three decades. Europe faces a critical challenge in securing a stable supply of these essential resources. This challenge is where EIT RawMaterials steps in. EIT RawMaterials is at the forefront of driving innovation and sustainability in the raw materials sector. As an Innovation Community within the European Institute of Innovation and Technology (EIT), we collaborate with over 300 partners across industry, academia, and research institutes. Together, we forge dynamic, long-term partnerships to develop innovative solutions that meet Europe's growing demand for raw materials Position Role Overview The Data Analyst (m/f/d) in the Innovation and Product Development Team supports data‑driven decision‑making by transforming complex commodities and market data into clear, actionable insights. The role focuses on building high‑quality dashboards, analysing trends in minerals and metals markets, and working closely with internal stakeholders to deliver reliable reporting, meaningful insights, and self‑service analytics solutions that support innovation, strategy, and product development activities. Tasks and Responsibilities 1. Data Visualisation & Reporting Design, build and maintain interactive dashboards and reports (e.g. in Power BI, Tableau, Qlik, or similar tools) for internal stakeholders. Translate complex commodities market data (esp. minerals and metals) into clear, user-friendly visual narratives . Develop standardised reporting templates and data visualisation guidelines to ensure a consistent look-and-feel and interpretation. Implement self-service BI solutions , enabling non-technical users to explore and filter data independently. Continuously improve and iterate on dashboards based on user feedback and changing business needs. 2. Data Analysis & Insights Generation Perform exploratory data analysis on commodity price data, market indices, supply-demand indicators, and other relevant datasets. Identify patterns, trends, correlations, and anomalies in raw, advanced and critical materials markets. Prepare ad-hoc analyses and visualisations to support strategic projects, management decisions, and external reporting. Develop KPIs and metrics to monitor market developments and internal performance indicators. Summarise findings in clear, concise insight reports , including visual storytelling and key messages tailored to the audience. 3. Data Management & Engineering Collaboration Work closely with other teams or experts to ensure reliable data pipelines , from data ingestion to visualisation. Contribute to the design of data models, semantic layers, and datasets optimised for BI and visualisation tools. Help define and maintain data quality checks (e.g. completeness, consistency, timeliness) for commodities and market data. Support integration of external data sources (market feeds, APIs, databases, files) into the analytics environment. Document data sources, definitions, transformations and business logic used in dashboards and reports. 4. Stakeholder Engagement & Consulting Act as a trusted partner for internal stakeholders (e.g. business development, strategy, innovation, procurement, finance). Work with stakeholders to refine requirements , prioritise dashboard features, and understand decision-making needs. Provide training and guidance to colleagues on how to interpret and use dashboards and visualisations. Present key findings and dashboards to non-technical audiences , explaining methodology, assumptions, and limitations. Critically review data to spot errors, inconsistencies, and anomalies in large datasets. Collect and structure user fee