Senior Finance Analyst, Strategic Finance

About the role

This role partners with cross-functional leaders to convert business objectives into actionable plans. You translate operational data into insights that guide strategic decisions for a global SaaS workforce focused on hourly employees. The position focuses on modernizing finance through automation and AI to serve a global frontline workforce.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

The team constructs financial models that translate strategy into measurable targets for leaders. Modeling clarifies trade-offs so stakeholders see the financial impact of their choices.

Workflows incorporate automation and AI to design forecasting processes that improve accuracy and speed. Forecasting processes integrate automation and AI to enhance accuracy and speed for operational planning.

Operational data is consolidated into dashboards that reveal how people, shifts, and compliance affect business outcomes. Dashboards consolidate operational data to monitor health and prioritize fixes for people and shifts.

The end-to-end employee lifecycle is analyzed to link workforce behavior with financial results. Analysis of the end-to-end employee lifecycle links workforce behavior with financial results for staffing and scheduling.

Complex operational metrics are translated into narratives for non-finance leaders to act on. Narratives translate complex operational metrics so non-finance leaders can act on them effectively.

Scenario planning guides investment and capacity decisions in a high-growth environment for the business.

The business is enabled with self-service insights and responsible AI guidance. Self-service insights and responsible AI guidance enable teams to adopt tools while maintaining governance and data quality.

Requirements

The posting states a bachelor's degree requirement. A degree is required.

This range reflects the depth needed to operate in a fast-growing SaaS environment.

Proficiency in Excel and data modeling, including structuring large datasets for analysis. Logic remains transparent and maintainable when modeling structures are used for data.

Experience with workflow, automation, and AI-enabled tools to enhance reporting and decision-making. Tool experience improves the reliability and timeliness of insights for finance operations.

Ability to partner with cross-functional leaders and influence without direct authority. Priorities align across product, operations, and compliance through influence in decision processes.

Communication skills explain financial concepts to non-finance stakeholders. Data becomes shared understanding when clarity turns complex concepts into action for stakeholders.

Practical notes

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Roles in finance operations increasingly rely on data fluency and comfort with modern tooling. Fluency in querying, modeling, and visualization helps teams turn raw information into decisions.

Automation and AI change how analysts validate assumptions and iterate on forecasts. Understanding these tools lets you challenge methodology and improve outputs for planning.

Cross-functional collaboration is common in global SaaS companies, where teams align around metrics that span product, compliance, and operations. Shared metrics create coherent decision frameworks for finance and operations.

Data-driven storytelling shapes how leaders discuss trade-offs and prioritize initiatives. Clear narratives make complexity actionable for busy stakeholders in fast-moving environments.

Cloud platforms and modern data stacks support the analysis performed in this role. These technologies enable scalable, auditable insights across the business for finance and operations.