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# Data Analyst - Finance

- **Company:** Emma
- **Location:** Frankfurt am Main, Germany
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Computer Programming, Extract Transform Load (ETL), Data Warehousing, Python (Programming Language), Power BI, SQL Databases, Tableau (Software), Git, Information Technology, Software Version Control
- **Published:** October 22, 2026
- **Apply:** https://www.adzuna.de/details/5936849705

## About the Role

* Required Qualifications Bachelor's/ Master's / PhD in Economics, Computer Science, Data Science, Mathematics, Business Informatics, or a related quantitative field. Additional certifications are a plus (CFA, CFI etc.)
 * Proven 3+ years of experience in analytics or analytics engineering, ideally in a Finance, Fintech, or commercial context. Exposure to data science is a plus.
 * Excellent programming skills in SQL and Python.
 * Familiarity with version control (Git).
 * Strong experience with at least one major BI/visualization tool: Tableau, Power BI, or equivalent.
 * Hands-on experience with dbt and a solid understanding of data warehousing concepts and ETL best practices.
 * Previous experience with Paradime is a plus.
 * Experience working with modern cloud data warehouses (Redshift etc) is a plus .
 * Strong quantitative and analytical reasoning skills.
 * Clear written and verbal communication; able to explain technical concepts to non-technical stakeholders.
 * Collaborative, team-first mindset.

## Description

* Partner closely with Finance team, Engineering and Business stakeholders to translate ambiguous questions into clear analytical deliverables.
 * Analyze data (primarily financial), generate insights, and set up dashboards that convey them clearly and intuitively to stakeholders.
 * Write production-grade SQL and Python to support analytics workflows and reusable analytical tooling.
 * Design, build, document and maintain scalable data models using dbt (in Paradime) to support finance and cross-functional reporting needs.
 * Automate data collection, cleaning, transformation, and deployment, ensuring data is reliably available for analysis.
 * Contribute to data quality, testing, documentation, and engineering standards across the analytics stack.
 * Mentor more junior team members on SQL, modeling best practices, and analytical rigor.
 * Partner with a talented group of Data Engineers, Analysts, and Scientists, contributing to and learning from their diverse experience and skills.