Sales & Revenue Data Analyst
New
USAFull-TimeSenior
SalaryCompetitive Salary and Equity Opportunities
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Job Details
- Experience
- 5 years
- Required Skills
- PythonSQLMicrosoft Power BITableauData visualizationLookerR
Requirements
- Bachelor's or Master's in Business, Finance, Statistics, Economics, or a related field.
- 5 years of experience in revenue analytics, sales operations, business intelligence, or a related role with end-to-end ownership of analytical projects.
- Excellent communication and storytelling skills, with the ability to present complex analysis to commercial and non-technical stakeholders alike.
- Expert data visualization skills, with a strong understanding of data visualization tools such as Omni (preferred), Looker, Domo, Tableau, or Power BI.
- Deep expertise in SQL for data extraction, manipulation, and analysis across medium to large datasets.
- Experience with Python or R for data analysis and modeling.
- Demonstrated ability to build forecasting models and pipeline reporting frameworks in a B2B environment.
- Strong attention to detail and a bias toward data-driven decision making.
Responsibilities
- Own pipeline reporting and revenue forecasting, partnering closely with sales leadership to track performance against targets and surface risks and opportunities early.
- Build and maintain pricing and deal-analysis frameworks to evaluate deal structure, margins, and competitiveness across new and existing client opportunities.
- Develop internal reporting that supports retention, expansion, and renewal conversations, giving account teams the data they need to act with confidence.
- Analyze marketing performance data to evaluate channel effectiveness, lead quality, and contribution to the pipeline, helping optimize spend and prioritization.
- Collaborate with cross-functional teams to translate business questions into structured analyses and present findings to stakeholders in a clear, compelling way.
- Create and automate dashboards and reports to ensure the revenue team has timely, accurate visibility into performance metrics.
- Partner with engineering and data teams to ensure the underlying data pipelines and models powering revenue analytics are accurate and well-maintained.
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