Data Analyst, Finance Analytics

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Data Analyst, Finance Analytics based in Canada. We are seeking a detail-oriented Data Analyst, Finance Analytics to transform complex financial and business data into reliable insights that support strategic decision-making. In this role, you will work across key financial metrics, including annual recurring revenue (ARR), customer retention, subscriptions, billing, and customer cohorts, helping teams understand business performance and identify opportunities for growth. You will build interactive dashboards, develop trusted data models, and deliver self-service reporting for Finance, Sales, Marketing, and Revenue Operations. You will also help modernize reporting workflows by leveraging AI-powered tools and automation to reduce manual tasks and accelerate analysis. Working closely with Analytics Engineering and cross-functional stakeholders, you will improve data quality, strengthen metric consistency, and support evolving pricing strategies and business models. This is an opportunity to combine technical expertise, analytical thinking, and business collaboration in a remote-first environment that values innovation, continuous learning, and data-driven decision-making.

Financial and business analytics: Analyze financial and operational data across annual recurring revenue, paying customer accounts, subscriptions, retention, billing, and customer cohorts to identify trends, explain performance changes, and generate actionable insights. Dashboard development and reporting: Build, maintain, and continuously improve interactive dashboards and reporting solutions that enable stakeholders to explore financial and business performance independently. Business requirements and stakeholder collaboration: Translate business questions into clear analytical deliverables by gathering requirements, clarifying objectives, incorporating stakeholder feedback, and ensuring reporting meets business needs. Financial reporting support: Support month-end and quarter-end reporting cycles by investigating variances, identifying key performance drivers, validating figures, and communicating the reasons behind changes in financial results. Data modeling and metric governance: Contribute to reliable, scalable data models using established modeling practices, including fact and dimension tables and star schemas, ensuring consistent metric definitions across reporting environments. Data quality and analytics engineering: Partner with Analytics Engineering to productionize reporting datasets, improve data reliability, investigate discrepancies, and maintain trusted sources for financial and operational analysis. AI-enabled workflow automation: Use AI tools and intelligent workflows to automate repetitive reporting activities, reduce spreadsheet-based manual work, accelerate research, and improve analytical productivity while maintaining accuracy and appropriate controls. Cross-functional business support: Collaborate with Finance, Sales, Marketing, Revenue Operations, and Data and Analytics teams to address analytical needs and provide insights that inform business planning and operational decisions. Pricing and business model support: Develop and maintain data foundations that accommodate new pricing strategies, product offerings, subscription structures, and evolving business models. Analytical communication: Present findings, explain complex data clearly, document methodologies, and provide recommendations that help stakeholders make informed decisions. Continuous improvement: Identify opportunities to enhance reporting processes, improve analytical tools, strengthen data definitions, and increase the accessibility and usefulness of financial insights. Requirements Relevant experience: Experience in data analytics, business intelligence, financial analytics, or a related data-focused role, with the ability to deliver reliable analysis and reporting for business stakeholders. SQL proficiency: Strong working knowledge of SQL, including joins, common table expressions (CTEs), window functions, and querying and analyzing large datasets. Data modeling knowledge: Understanding of data modeling principles, including fact and dimension tables, star schemas, and other approaches used to create consistent and trustworthy analytical datasets. Analytical problem-solving: Strong diagnostic and critical-thinking skills, with the ability to investigate discrepancies, identify trends, validate assumptions, and explain complex findings accurately. Business requirements gathering: Ability to translate stakeholder questions into well-defined analytical tasks, gather requirements, develop appropriate reporting solutions, and refine dashboards based on feedback. Financial and SaaS metrics: Experience working with Finance, Sales, or go-to-market data is an advantage. Familiarity with SaaS metrics such as ARR, customer retention, subscriptions, recurring revenue, and customer cohorts is particularly valuable. Business intelligence tools: Experience developing interactive dashboards and analytical workflows using tools such as Hex or comparable business intelligence platforms. AI and productivity tools: Familiarity with AI-enabled tools such as Claude Code, Cursor, or similar platforms, with an interest in applying AI to research, reporting, analysis, and workflow automation. Data transformation and enterprise systems: Exposure to dbt and enterprise data sources such as Salesforce, Stripe, or NetSuite is an advantage. Attention to detail: Strong commitment to data accuracy, reliable reporting, clear documentation, and consistent metric definitions. Communication and collaboration: Ability to explain analytical findings to both technical and non-technical audiences and work effectively with cross-functional teams. Adaptability and continuous learning: Comfort working in a fast-paced, evolving environment, navigating ambiguity, incorporating feedback, and experimenting with new tools and methods. Responsible AI practices: Willingness to use AI thoughtfully while maintaining accountability for analytical accuracy, data confidentiality, sound judgment, and appropriate controls. Benefits Competitive compensation in Canada: Annual base salary ranging from CAD $83,000 to CAD $116,000 , depending on experience, qualifications, and skill set. Equity compensation: An equity grant as part of the overall compensation package. Incentive opportunities: Eligibility for applicable incentive programs in addition to base salary and equity. Comprehensive healthcare: Health, dental, and other benefits designed to support employee well-being. Retirement savings: Access to a retirement savings plan with employer matching through the applicable program. Generous paid time off: A flexible paid time off policy to support personal needs, rest, and work-life balance. Parental support: Maternity and parental leave top-up programs to support employees and their families. Professional development: Opportunities to learn new technologies, develop analytical expertise, and contribute to innovative AI-enabled workflows. Remote-first work environment: The flexibility to work remotely from your home country, with occasional travel for team meetings, company-wide gatherings, or relevant events as required. Employee recognition: Peer-to-peer recognition and rewards through Bonusly and other recognition initiatives. Community involvement: Paid volunteer days to support causes that matter to you. Additional perks: Access to a complimentary password manager account and other employee benefits. Inclusive workplace: A commitment to diversity, equity, accessibility, and reasonable accommodations throughout the recruitment process and employment.