Data Analyst – Finance

Satispay

Data Analyst improving decision-making through financial analytics and reporting for Satispay. Collaborating with Finance and key stakeholders to drive impactful business solutions.

Posted 6/23/2026full-timeMilan • 🇮🇹 ItalyMid-LevelSenior💰 €40,000 - €50,000 per yearWebsite

Tech Stack

Tools & technologies

ERPETLPythonSQL

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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 Fluency — Comfortable using AI tools day-to-day for analytics, automation, and generating ad-hoc analyses, C-level summaries, marketing campaign suggestions, and business cases efficiently using AI agents. Basic to mid-level proficiency with Claude is a strong plus. Experience interacting with agents programmatically via APIs is a plus.
Analytical & Problem-Solving Skills — Comfortable with ambiguity, able to break down complex financial problems into focused workstreams and deliver evidence-based answers. Takes end-to-end ownership of analytical projects: from problem framing to stakeholder recommendations, monitoring business impact throughout.
Stakeholder Management & Communication — Able to independently lead initiatives across Finance and Marketing domains, efficiently translating technical findings into clear, actionable recommendations for senior stakeholders.
Unit Economics & Monetisation Knowledge — Familiarity with consumer lifecycle metrics (Acquisition, Activation, Retention, Monetisation) and user economics (ARPU, CAC, LTV, Payback Period) approached from a financial planning and user-value lens. Experience with Fintech-specific KPIs and sector dynamics is a strong plus.
Data Visualisation — Experience with Hex, Looker, or similar tools to build clear and insightful financial data representations.

Benefits

Comp & perks

Health (Private insurance for you and your family, psychological support with Serenis, mental health workshops)
Financial resources (Stock Option Plan, Meal vouchers, Relocation support if you’re moving countries)
Growth and development (Professional development programs, Internal mobility, Language courses with Preply)
Flexibility (Unlimited PTO, Hybrid working policy*, Flexible working hours)
Family (Enhanced parental leave, Additional leave for child sickness)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

SQLPythondbtdata modellingETLfinancial data modellinganomaly detectiondata transformationAI toolshigh-volume data environments

Soft Skills

analytical skillsproblem-solving skillsstakeholder managementcommunication skillsownershipcollaborationtranslating technical findingsnarrative buildingambiguity managementactionable recommendations