Analyst, Sales Operations

About Spot On

We're not just building restaurant tech, we're giving independent restaurants the tools to compete and win. From our award-winning point-of-sale to AI-powered profit tools, everything we do helps operators boost profit, work smarter, and keep their best people. And every solution is backed by real humans who actually give a sh
* t about helping restaurants succeed.

• Named the #1 Restaurant POS by G2 (Fall 2025), based on ratings from real users
• Rated the top-rated point-of-sale (POS) for restaurants, bars, retail, and small businesses by Capterra users
• Awarded Great Places to Work and Built In's Best Workplaces for multiple years running

We move fast, care hard, and fight for independent restaurant operators to do what they love, and love doing it. If you're looking to make an impact with heart and hustle, Spot On is the place for you.

The Sales Operations team at Spot On comprises enthusiastic individuals dedicated to making a meaningful impact. We foster a culture of mutual support and encouragement, welcoming challenges and holding each other to high standards.

Our commitment to teamwork, collaboration, and expertise drives us to continuously push boundaries and strive for excellence.

This position follows a hybrid work schedule in accordance with the Company's in-office work policy.

What You'll Do

• Own lead data health in Salesforce, including deduplication, enrichment, data integrity, and ongoing CRM hygiene.
• Build and maintain reporting that provides visibility into lead quality, sales follow-up, and lead management effectiveness.
• Translate complex data and model outputs into clear, actionable recommendations that improve lead performance, sales efficiency, and strategic decision-making.
• Measure the effectiveness of lead management initiatives and recommend optimizations based on performance data.
• Partner with stakeholders to improve how leads are identified, prioritized, and worked across the organization.
• Leverage Salesforce, SQL, and automation tools to streamline lead management processes and reduce manual work.
• Integrate AI-powered tools and automation workflows to reduce manual data work, improve process reliability, and enable faster, more scalable operations.
• Apply statistical modeling, segmentation analysis, and predictive analytics to identify trends in lead behavior, conversion likelihood, and pipeline health.
• Leverage AI and large language model (LLM) tools to accelerate data analysis, automate insight generation, and surface anomalies or patterns at scale.

What Success Looks Like

• Lead data is accurate, enriched, and trusted across the organization.
• Lead scoring and prioritization frameworks help sales teams focus on the highest-value opportunities.
• Reporting provides clear visibility into lead performance and conversion trends.
• Manual processes are reduced through automation and process improvements.
• Sales, Marketing, and Revenue Operations teams view you as a trusted partner for lead management strategy and execution.
• Predictive models and data science outputs are actively used by leadership to guide lead strategy, resource allocation, and go-to-market decisions.
• AI tools are embedded into the team's workflow, reducing time spent on manual analysis and freeing capacity for higher-impact strategic work.

What You'll Bring

• 2-4 years of experience in Revenue Operations, Sales Operations, Marketing Operations, Lead Management, or a related analytical role.
• Strong analytical and problem-solving skills with the ability to turn data into actionable insights.
• Experience working in Salesforce and building reports and dashboards.
• Strong proficiency in SQL for complex data extraction and manipulation; experience with Python or R for statistical analysis and modeling strongly preferred.
• Strong attention to detail and commitment to data accuracy.
• Ability to manage multiple priorities and work independently in a fast-paced environment.
• Excellent communication and cross-functional collaboration skills.
• Hands-on experience with data science techniques including predictive modeling, clustering, regression analysis, and feature engineering applied to business or marketing datasets.
• Comfortable working with…