Analyst, GTM Sales Intelligence

As a GTM Sales Intelligence Analyst, you will partner closely with Sales leadership, Sales Operations, and Revenue Operations to help Apollo make better go-to-market decisions. You will own core sales reporting and forecasting, develop analytics that improve pipeline visibility, and build the dashboards and metrics that Sales leaders use to manage the business.

This role sits within the GTM Analytics team and is focused on turning CRM and pipeline data into actionable insights. You will work across Sales, RevOps, Finance, and Data Engineering to improve forecast accuracy, surface growth opportunities and risks, and ensure stakeholders can trust the data behind their decisions.

This is a strong fit for an analyst who enjoys solving business problems with data, building scalable reporting systems, and partnering directly with senior stakeholders.

• Build and maintain pipeline reporting that gives Sales leadership visibility into deal health, stage progression, and conversion trends.
• Identify patterns in pipeline performance and proactively surface risks, opportunities, and coaching insights that improve forecast quality and sales execution.
• Partner with Sales leadership and RevOps to develop scalable forecasting models that blend CRM data with historical patterns and business signals. Establish a regular cadence of forecast reporting that earns trust through accuracy and consistency. Build and evolve forecasting capabilities. 
• Own the Sales dashboard ecosystem, from AE activity tracking to executive business reviews. Ensure reporting is accurate, accessible, and evolves alongside the needs of the organization. 
• Capture analytics and metrics requests from Sales stakeholders, triage and prioritize them, and translate business questions into structured reporting requirements. Work with the broader analytics and data engineering team to incorporate high-priority needs into the reporting layer. Be the analytics translation layer for the Sales org. 
• Identify gaps or inconsistencies in CRM data that affect reporting fidelity, surface them to the right owners, and partner on remediation. Build systems that make data hygiene invisible and convenient for reps and ops. Improve data quality at the source. 

• 3+ years of experience in an analytics, Sales Operations, or Revenue Operations role, with hands-on experience building reports and dashboards in a B2B SaaS environment.
• Proficiency in Salesforce—comfortable navigating Opportunity objects, pipeline data, and activity reporting. You understand how SFDC data flows and where it breaks.
• Strong SQL skills and experience working with BI tools (e.g., Looker, Tableau, or similar). Able to build dashboards independently and write queries without significant support.
• A translator's instincts. You know how to take a vague business question from a Sales VP and convert it into a clean, scoped analytics request—and you know when and how to push back on scope.
• Experience with forecasting methodologies or a genuine interest in building forecasting capability from the ground up.
• A builder's bias toward process improvement. When you find a recurring gap in reporting or data quality, your instinct is to fix the root cause, not just patch the output.
• Comfortable operating across functions—you'll work with Sales, RevOps, Data Engineering, and Finance, and you need to partner effectively with all of them.
• This role is ideal for an analyst or technical ops professional looking to deepen their exposure to analytics, intelligence, and data infrastructure, and step back from day-to-day operational execution.

Apollo operates at the intersection of AI and go-to-market, and our analytics team is expected to lead from the front. This role requires genuine fluency in leveraging AI tooling. That means:

• Using LLMs as an active part of your analytics workflow. Whether it’s generating and debugging SQL, summarizing pipeline or forecast data, or accelerating the build of dashboards and documentation—you should be reaching for AI tools wherever they can accelerate the completion of tasks.
• Structuring and exposing data for AI interpretation. Understanding how to make sales and pipeline data clean, well-labeled, and accessible so that AI tools can reason over it reliably. This includes thinking about schema design, field definitions, and semantic documentation.
• Accelerating output with AI-assisted development. Using AI to compress the time from question to answer. We expect analysts at this level to leverage AI to raise their own output ceiling.
• Staying current as the tooling evolves. The AI tooling landscape is moving fast. We want analysts who are curious, self-directed learners—people who experiment, share what works, and help raise the floor for the whole team.
• Navigating AI’s limitations and pitfalls. Understanding where AI-generated outputs can introduce errors, bias, hallucinations, or false confidence, and implementing validation processes to ensure analytical rigor. You know when to trust AI, when to verify its work, and when to rely on first-principles analysis instead.