We are seeking a Senior Sales Analyst to serve as a keyanalytics resource for our Sales and Marketing teams. This role will beresponsible for transforming commercial data into actionable insights thatdrive revenue growth, improve sales effectiveness, and sharpen our go-to-marketstrategy. The Senior Sales Analyst will own sales performance reporting,pipeline analytics, and marketing ROI analysis - partnering closely with ourChief Revenue Officer and Sales and Marketing leadership. This role will benefitfrom the foundation built by our broader analytics and AI organizations andwill ultimately be responsible for continuing to improve how commercialdecisions are made.
Key Responsibilities- Sales Performance Reporting: Own the design, development, and delivery of recurring sales reports and analytics covering revenue by service line, rep-level performance, quota attainment, pipeline health, and customer retention - ensuring Sales leadership has timely, accurate visibility into the results and opportunities.
- Sales Growth Analysis: Identify revenue growth opportunities by analyzing trends across accounts, segments, geographies, and service lines using both internal and external data sources. Surface patterns in new logo acquisition, churn, wallet share, and cross-sell to help sales teams focus their energy where it matters most.
- Marketing & Pipeline Analytics: Support the Marketing function with campaign performance analysis, lead source attribution, and digital channel ROI. Partner with Marketing to connect top-of-funnel activity to downstream pipeline and closed revenue, informing how and where to invest for growth.
- Sales & Marketing Enablement Tools: Build and maintain self-service tools with the help of AI and our software engineers that put actionable data directly in the hands of our Outside Sales Team, Inside Sales, and Marketing. Reduce manual reporting burdens and create scalable tools the commercial team can rely on day-to-day.
- CRM & Data Integrity: Partner with Data Engineering to ensure CRM data and underlying transaction data are clean, consistent, and structured to support reliable commercial reporting. Flag data quality issues and drive resolution to maintain the integrity of sales analytics.
- Forecasting & Predictive Insights: Develop and maintain sales forecasting models and apply predictive analytics to identify at-risk accounts, high-potential prospects, and seasonal revenue patterns. Leverage AI and advanced modeling where appropriate to give the commercial team a forward-looking edge.
- Cross-Functional Collaboration: Work closely with Sales leadership, Marketing, Sales Operations, and Finance to align on metrics definitions, reporting cadences, and analytical priorities. Translate complex findings into clear narratives and recommendations that drive action at the leadership level.
- Tool and Technology Evaluation: Stay abreast of emerging analytics and AI tools and technologies, recommending and implementing solutions that enhance the company's analytics and commercial capabilities.
Qualifications:- Bachelor's degree in Data Science, Statistics, Computer Science, Business Analytics, Math, Sciences, or a related field (Master's degree preferred).
- 2+ years of experience in analytics, commercial operations, or a related business analytics role
- Proven track record of driving data-driven decision-making and building analytics solutions in a fast-paced environment.
- Proven track record of developing insights that lead to action and positive results
- Experience implementing AI-driven analytics, machine learning models, or predictive analytics in a business context.
- Strong proficiency in Claude or other LLM development tools, SQL, R, Excel, PowerPoint.
- Experience collaborating with Data Engineering teams to design and implement data views.
- Experience working with CRM platforms (e.g. HubSpot, Salesforce) and familiarity with sales engagement or marketing automation tools (e.g., Apollo.io, Clay, Klaviyo).
- Excellent communication skills, with the ability to translate complex data and AI insights into clear, actionable recommendations for non-technical stakeholders.
- Strong project management skills, with the ability to prioritize and manage multiple initiatives simultaneously.
- General understanding of data governance, data quality, and best practices in analytics.
- Knowledge of data warehousing concepts and ETL processes.
