Senior Data Analyst, Strategic Finance
New
J
JobgetherStrategic Finance
Based in the United StatesFull-TimeSenior
Salary$133,000–$156,000
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Job Details
- Experience
- 4+ years of experience in data analysis, business intelligence, financial analytics, or an equivalent analytical function.
- Required Skills
- PythonSQLBusiness IntelligenceTableauData visualizationFinancial analysisdbt
Requirements
- 4+ years of experience in data analysis, business intelligence, financial analytics, or an equivalent analytical function.
- Experience or exposure to FP&A, Accounting, SaaS metrics, or strategic finance is a strong advantage.
- Strong proficiency in SQL, with experience working with large and complex datasets.
- Hands-on experience with data visualization and business intelligence tools such as Tableau, Sigma, or comparable platforms.
- Familiarity with dbt is preferred, with Python experience for advanced analysis considered a plus.
- Strong analytical and problem-solving abilities.
- Proven experience working cross-functionally and building productive relationships with stakeholders.
- Strong communication skills, both written and verbal.
- Bachelor’s degree in Data Analytics, Statistics, Informatics, Finance, Economics, Business, or a related discipline, or equivalent relevant experience.
- Curious and open-minded about using AI to amplify analytical capabilities.
Responsibilities
- Conduct deep, prioritized analyses that uncover strategic insights and inform high-impact financial and business decisions.
- Serve as a technical and analytical partner to Finance, FP&A, Accounting, RevOps, Product, Marketing, and other cross-functional teams.
- Translate complex datasets into clear recommendations that support planning, forecasting, investment decisions, and scalable growth.
- Build self-service dashboards, reporting solutions, and reusable data assets that empower stakeholders to make informed decisions independently.
- Identify opportunities to automate recurring financial and analytical deliverables, improving efficiency and scalability.
- Partner with Data Engineering and analytics teams to improve data quality, accessibility, and the overall analytics infrastructure.
- Communicate analytical findings clearly to both technical and non-technical audiences.
- Use AI responsibly to accelerate analysis, automate workflows, and strengthen the quality of work.
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