Analyst, GTM Customer Intelligence

Apollo.io

1d•$126,700 - $182,200•Remote

About The Position

As a GTM Customer Intelligence Analyst, you will be an embedded analytics partner to Apollo's Customer Success organization and CS Operations team. You will build the post-sales intelligence layer that powers retention, expansion, and customer health decision-making—from health scoring and renewal analytics to billing insights, support analytics, and the dashboards that CS leadership depends on to manage the business. This is a high-impact role for an analyst or technical ops professional who wants to go deep on post-sales analytics and intelligence. You will report directly to the GTM Analytics Manager and work closely with CS leadership, CS Operations, Finance, and data engineering teams to translate complex customer lifecycle questions into reliable, scalable reporting.

Requirements

3+ years of experience in an analytics, Customer Success Operations, or Revenue Operations role, with direct exposure to post-sales data in a B2B SaaS environment.
Familiarity with CS data domains—health scoring, NRR, churn, renewal pipelines, support metrics, and customer lifecycle stages. You don't need to have built all of these from scratch, but you should understand what they are and why they matter.
Strong SQL skills and experience working with BI tools (e.g., Looker, Tableau, or similar). Able to build and own dashboards independently.
Experience with Salesforce or a CS platform (Gainsight, Vitally, ChurnZero, or similar). Understanding of how CS data is captured and where the quality gaps tend to live.
A translator's instincts. You know how to take a retention question from a CS VP and convert it into a clean, scoped analytics project—and you know when and how to push back on scope.
Analytically rigorous and detail-oriented. Post-sales data is messy—billing records, support tickets, and CRM data rarely agree out of the box. You need to enjoy untangling it and managing the organization’s expectations.
Comfortable operating across functions—you'll work with CS, CS Ops, Finance, Data Engineering, and occasionally Sales, and you need to collaborate effectively with all of them.
Genuine fluency in leveraging AI tooling.

Nice To Haves

This role is ideal for an analyst or technical ops professional looking to deepen their exposure to analytics, intelligence, and data infrastructure, with a focus on the customer lifecycle.

Responsibilities

Build and maintain the analytics infrastructure behind Apollo's customer health scoring model. Partner with CS leadership to validate scoring logic, surface at-risk customers, and identify leading indicators of churn or expansion. Turn health data into actionable insights for GTMEs and CS leadership. Own health scoring analytics and improvement.
Develop renewal pipeline reporting that gives CS and Finance visibility into renewal timing, risk, and expected outcomes. Establish recurring reporting cadences and work with CS Ops to identify where to intervene early. Build renewal analytics that drive predictable retention.
Instrument and analyze data from billing systems, support ticket volumes, and other post-sales touchpoints to identify patterns that predict churn or expansion. Surface these signals into CS workflows and dashboards so teams can act on them. Build analytics around billing, support, and adjacent retention signals.
Build reporting around upsell and cross-sell motion—including expansion pipeline, seat growth, product adoption metrics, and net revenue retention. Help CS and Sales leadership understand where expansion opportunity is concentrated and how to accelerate it. Support expansion analytics.
Build, maintain, and iterate on dashboards that serve the full CS org—from individual GTME book-of-business views to VP-level QBR decks. Ensure data is accurate, timely, and actionable. Design and own the CS analytics dashboard ecosystem.
Capture analytics and metrics requests from CS and CS Ops stakeholders, triage and prioritize them, and translate business questions into structured reporting requirements. Incorporate high-priority needs into the reporting layer in partnership with data engineering. Be the analytics translation layer for CS Ops.
Using LLMs as an active part of your analytics workflow. Whether it’s generating and debugging SQL, summarizing customer health scores, renewal forecasts, or retention signals, 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 customer health, retention, and post-sales 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.