Principal Data Analyst - Accounting (m/w/d)

You don’t just report numbers - you question them. You dig into large financial datasets, surface what doesn’t add up, and translate raw ERP data into clear, actionable insights. If you’re energized by process gaps, reconciliation puzzles, and the satisfaction of clean, reliable data, this role was built for you.

WHAT YOU’LL DO

Data Analysis & Financial Accuracy

• Systematically analyze, validate and reconcile large volumes of booking data to identify inconsistencies, outliers, and process gaps
• Detect system-side inconsistencies through structured analytical routines
• Develop and maintain automated control mechanisms to ensure ongoing data quality
• Perform large-scale reconciliations across ERP, BI tools, and other systems

Reporting & Analysis

• Support month-end, quarter-end, and year-end close activities through data-driven checks
• Handle ad-hoc analyses for unexplained booking discrepancies or system anomalies
• Support the Accounting team in developing reporting solutions — both systematic and ad-hoc

Process Optimization & Automation

• Design and implement (semi-)automated validation scripts and control routines to reduce manual effort
• Contribute to identification of process gaps and development of improvement proposals
• Support implementation of ERP changes from a data quality perspective

WHAT YOU BRING

Must-Have

• Degree in Business Administration, Finance, Data Science, or a comparable field
• Experience in data analysis, controlling, or accounting
• Experience with Power BI or comparable BI tools (Tableau, Looker)
• Knowledge of SQL for data analysis
• Strong Excel
• Hands-on experience with ERP systems (SAP preferred) and financial booking data
• Solid understanding of financial accounting processes and booking logic
• Analytical mindset: ability to recognize patterns in large datasets and draw meaningful conclusions
• Fluent English (written and spoken)

Nice-to-Have

• Fluent German

Who You Are

• Detail-oriented with a high personal quality standard — you notice what others overlook
• Structured and self-directed, even with complex or unstructured datasets
• Proactive: you identify problems before they escalate

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