Data Analyst, Finance Analytics

Job description

- Develop financial dashboards and reporting: Build, maintain, and enhance interactive dashboards that help stakeholders understand financial and business performance, identify trends, and make informed decisions. - Translate business questions into insights: Partner with internal stakeholders to understand analytical needs and deliver self-service reporting and actionable analysis across ARR, paying customer accounts, retention, subscriptions, and customer cohorts. - Strengthen financial data foundations: Contribute to reliable data models that support evolving pricing strategies, new product lines, and changing business models while maintaining consistent definitions for key metrics. - Support financial reporting cycles: Assist with month-end and quarter-end reporting by investigating variances, identifying performance drivers, and explaining changes in financial results. - Automate analytical workflows: Use AI tools and automation to reduce repetitive manual tasks, improve spreadsheet-based processes, and increase the speed and reliability of reporting. - Improve data quality and governance: Collaborate with Analytics Engineering to productionize reporting sources, validate data, resolve discrepancies, and maintain trusted analytical datasets. - Collaborate across departments: Present findings, incorporate stakeholder feedback, and work closely with Finance, Sales, Marketing, Revenue Operations, and Data Analytics teams to ensure reporting meets business needs. - Promote continuous improvement: Identify opportunities to refine dashboards, optimize analytical processes, and introduce more efficient, scalable approaches to financial analysis and reporting. ## Requirements - Analytics experience: Previous experience in data analytics or a related analytical role, with the ability to investigate complex datasets and translate findings into clear, actionable recommendations. - Strong SQL proficiency: Hands-on experience writing SQL queries using joins, common table expressions (CTEs), window functions, and other techniques to analyze large datasets. - Data modeling knowledge: Understanding of data modeling principles, including fact and dimension tables, star schemas, and methods for building consistent, reliable data structures. - Business and stakeholder collaboration: Ability to gather requirements, understand business questions, communicate analytical findings, and improve reporting solutions based on stakeholder feedback. - Financial and SaaS analytics: Experience working with Finance, Sales, or go-to-market data is an advantage, particularly in a SaaS environment. Familiarity with ARR, subscriptions, retention, billing, and customer cohort analysis is beneficial. - Business intelligence and AI tools: Familiarity with analytics and AI platforms such as Hex, Claude Code, Cursor, or comparable tools is an asset, along with an interest in applying AI to improve analytical workflows. - Data platform exposure: Experience with dbt and enterprise data sources such as Salesforce, Stripe, or NetSuite is considered an advantage. - Analytical problem-solving: Strong diagnostic skills, sound judgment, and attention to detail, with the ability to investigate discrepancies and identify underlying causes. - Communication and adaptability: Clear communication skills, a collaborative mindset, and the ability to manage changing priorities in a fast-paced environment. - AI responsibility: Willingness to use AI thoughtfully and responsibly, maintaining accuracy, confidentiality, data integrity, and appropriate controls while improving productivity. ## Benefits - Competitive US base salary: $95,000–$133,000 USD annually. - Equity compensation: Equity grant,