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Data Analyst - Finance

Satispay
CompanySatispay
CategoryData & Analytics
LocationMilan
RemoteHybrid
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted23 Jun 2026
Last verified30 Jul 2026
SourceEmployer career page (ashby)
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Description
About us Satispay began by rethinking the simple act of a payment to remove the friction from our daily routines. But we didn’t stop there. Today, we are building a complete financial platform designed to empower people and concretely improve their lives. By giving our 6 million users a clear, open path to pay, save, and invest, we are evolving into the definitive destination for every financial need. What you'll be doing As a Data Analyst, you will be a core member of our Growth & Marketing (G&M) team, taking the data and analytics lead on all Finance initiatives. You’ll play a key role in enhancing decision-making by developing analytical models and self-service data assets that drive impactful solutions for financial planning, reporting, and operational steering. Here's what your day-to-day will look like: - Build Financial Analytics & Reporting Capabilities — Develop P&L and cash flow analytics, plan vs. actual monitoring models, and financial KPI frameworks that enable Finance to steer the business with accurate, timely data. - Lead Investor Relations Analytics — Serve as the primary analytics and intelligence partner for Satispay's investors including top-tier VC firms, translating business performance into clear, data-driven narratives that maintain and strengthen those relationships. - Partner with Key Stakeholders — Act as the primary data and analytics partner for Finance, while collaborating closely with Marketing, Operations, and Product. Bridge the gap between financial data complexity and business decisions, translating technical findings into clear, actionable recommendations at the executive level. - Build High-Impact Visualisation Tools — Define key financial metrics and develop dashboards and reports that communicate financial insights effectively, empowering self-service across Finance and senior leadership. - Drive Business Economics Analytics — Partner with Finance to develop analytical frameworks across monetisation, unit economics, LTV, payback period, cost structure, and revenue drivers, bridging product performance and financial outcomes to support strategic planning. - Enhance Analytics Through AI-Powered Workflows — Design and implement AI-assisted workflows to automate anomaly detection, surface budget deviations, and unlock efficiencies in the analytical processes supporting budgeting and re-forecasting. - Contribute to Our Data Layer Evolution — As part of the broader G&M Analytics team effort, contribute to the development of our data mesh layer by designing financial data models and pipelines that integrate into the company’s federated data strategy, ensuring scalable and governed data ownership. Who we're looking for We need a problem-solver with a strong analytical mindset who thrives in a collaborative environment. If you’re detail-oriented and ready to take ownership in a dynamic setting, you’ll fit right in! Does this sound like you? - Relevant Experience — 5+ years in data-related roles spanning Data Engineering, Data Analytics, and BI, with at least 3 years of hands-on experience in high-volume data environments. Experience working in or closely with Finance, FP&A or Controlling functions is a strong plus. - Financial Data Expertise — Hands-on experience in financial data modelling, with a solid understanding of P&L structures, cash flow analytics, and plan vs. actual monitoring. Familiarity with ERP data schemas (e.g. SAP) and the ability to model and transform financial data for analytical use is highly valued. - Technical Proficiency — Strong command of SQL, dbt, and Python. Experience with Git and notebook-based analytics (e.g. Hex, Databricks, Jupyter) is a plus. Knowledge of SAP Public Could is also a plus. - Data Modelling & ETL — Proven experience designing data models, building ETL pipelines, and managing data transformation layers at scale, ideally in environments with high-volume, high-frequency financial data. - AI
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