Data Analyst, Finance Analytics

• 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, with participation in applicable incentive programs.

• Health and wellness: Comprehensive health insurance, including medical, dental, and other benefits.

• Retirement savings: 401(k) plan with employer matching.

• Time off: Generous paid time off to support rest and personal commitments.

• Family support: Maternity and parental leave top-up programs.

• Remote-first work: Flexibility to work remotely from within the United States, with occasional travel for team gatherings and in-person collaboration as required.

• Professional growth: Opportunities to develop analytical expertise, explore AI-enabled workflows, and contribute to impactful projects in a collaborative environment.

• Recognition and community: Peer-to-peer recognition programs and paid volunteer days.

• Additional perks: Access to a complimentary password manager account and a culture that values transparency, collaboration, continuous learning, and individual contributions.

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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