Finance Data Analyst

Accountabilities:: Map and document financial and corporate data sources, systems, ownership, definitions, and data flows to improve transparency and consistency. Identify data gaps, inconsistencies, duplication, and reconciliation issues across accounting, billing, CRM, HR, payroll, cash management, and expense-management systems. Define data requirements for monthly closing, budgeting, forecasting, and management reporting, ensuring key financial and operational metrics are clearly defined and consistently calculated. Explore, transform, and model data from data warehouses and connected business systems to create reliable, reusable, and well-documented datasets. Reconcile data across multiple sources and investigate discrepancies to improve the accuracy and reliability of financial and corporate reporting. Produce recurring financial analyses, dashboards, and reporting to support leadership decisions, including budget-versus-actuals analysis, variance analysis, cash monitoring, headcount planning, and SaaS performance analysis. Collaborate with data engineering teams and system owners to improve integrations, transformations, data accessibility, and quality checks. Automate recurring reporting, reconciliations, and analytical workflows where automation can deliver meaningful efficiency gains. Maintain documentation covering data lineage, metric definitions, business rules, assumptions, and known limitations. Contribute to data governance and quality monitoring, preparing reliable datasets for forecasting, anomaly detection, and future AI-driven use cases. Handle confidential financial, HR, and payroll information with discretion, accuracy, and a strong commitment to data protection. Requirements: At least 3 years of experience in data analysis, business intelligence, financial analytics, FP&A analytics, or a related analytical role. Strong analytical and problem-solving skills, with the ability to investigate data across multiple systems and identify the root causes of discrepancies. Good command of SQL and practical experience with at least one business intelligence or data visualization tool. Ability to translate business questions into clear data requirements, meaningful metrics, and actionable analyses. Solid understanding of financial concepts, including budgeting, forecasting, monthly closing, variance analysis, revenue, cash flow, costs, and unit economics. Advanced Excel or Google Sheets skills, including the ability to review, audit, and structure existing financial models. Strong documentation and communication skills, with the ability to collaborate effectively with both Finance and technical stakeholders. Professional fluency in English, both written and spoken, and the ability to communicate complex findings clearly. A rigorous, curious, and structured approach to data quality, with the confidence to challenge assumptions and make definitions explicit. Ability to work independently, prioritize competing requests, and deliver practical solutions in a changing business environment. Nice-to-have qualifications: Experience in a B2B SaaS, software, technology, or scale-up environment. Familiarity with SaaS revenue metrics, including MRR, ARR, NRR, churn, and bookings. Understanding of SaaS cost structures, including cloud storage, compute, data retention, AI-related expenses, and gross margin. Experience with finance, accounting, billing, CRM, HRIS, payroll, expense-management, or financial planning tools. Knowledge of data warehouses, data modeling, ETL processes, APIs, or data quality frameworks. Experience with Python or another programming language for analysis and automation. Previous exposure to sensitive HR or payroll data and an understanding of the confidentiality requirements involved. Candidates who meet most, but not necessarily all, of these requirements are encouraged to apply. A strong willingness to learn, analytical thinking, and the ability to demonstrate relevant skills are equally valued. Benefits: Full-time employment in a role with meaningful exposure to financial strategy and business performance. Opportunity to work in a technology-driven environment alongside Finance, Data Engineering, and other cross-functional teams. Scope to improve reporting processes, strengthen data quality, and contribute to automation and advanced analytics initiatives. Opportunities to develop expertise in financial analytics, business intelligence, data modeling, and SaaS performance metrics. A collaborative working environment that values clear communication, continuous improvement, and practical problem-solving. Potential exposure to future forecasting, anomaly detection, and AI-driven analytical projects. Salary, healthcare coverage, paid leave, flexibility, and other employment benefits to be confirmed with the hiring company.