AS
Sales Analytics Manager
Abnormal SecurityRemote - USAPosted 1 May 2026
Job Description
About the Role
Abnormal AI is looking for a Sales Analytics Manager to join the Field Operations team. This role owns the analytics ecosystem for all of sales, serving as the architect of how our revenue organization measures performance, forecasts pipeline, and makes data-driven decisions. The ideal candidate brings a strong AI-first mindset and hands-on expertise with Gong, Sigma Computing, and Snowflake to build scalable, self-service analytics that directly accelerate business outcomes. You will work cross-functionally with Sales Leadership, Finance, Data Engineering, and the CRO organization to elevate analytics maturity from descriptive reporting to predictive, AI-augmented intelligence.
What You Will Do
Lead the global rollout of Gong Forecasting across the full AE organization, driving platform adoption, building executive forecast dashboards for CRO-level visibility, and designing AE/RD 1:1 coaching dashboards for Sales Leadership
Own and evolve the Sigma Computing reporting ecosystem—including Activity Analytics, EOQ Retro, Metrics That Matter, and the GTM Reporting Catalog—replacing manual spreadsheet workflows with scalable, governed BI solutions
Apply an AI-first approach to analytics: lead a formal Sigma AI POC, identify high-impact use cases where AI reduces time-to-answer for Sales Leadership, and deliver a strategic FY27 roadmap for AI-driven self-service analytics built on Snowflake AI
Architect and execute the Salesforce Opportunity Split framework end-to-end—from technical requirements and Finance sign-off through pilot deployment, downstream impact analysis, and full Snowflake/Sigma data pipeline parity
Drive GTM Data Governance by owning the cross-functional Data Dictionary, relaunching the Reporting Council, and formalizing the KPI sign-off and metric codification process with Data Engineering
Define and document the strategic Reporting Strategy vision, including target-state outcomes, Gold Standard certified reports, and a scalable self-service analytics framework
Partner with Sales Enablement and Field Operations to build training programs, enablement materials, and adoption strategies that drive lasting behavior change across the revenue organization
Conduct deep-dive analyses to surface pipeline health trends, forecast risks, and performance gaps—translating findings into clear recommendations for Sales and GTM leadership
AI-Forward Mindset
AI tools are not optional in this role—they are core to how we work. We expect this person to actively use and evaluate AI capabilities across Sigma, Snowflake, and Gong to reduce manual work, accelerate insight delivery, and build more intelligent self-service experiences for the revenue organization. Candidates should be able to speak concretely about how they have applied AI tools in prior analytics or operations work, and bring a point of view on where AI can create the most leverage in a GTM analytics context.
Must Have Skills
5+ years in Sales Analytics, Revenue Operations, or GTM Analytics at a B2B SaaS company Required
Sigma Computing: production-level experience building and maintaining dashboards, workbooks, and self-service reporting layers for revenue teams Required
Gong: hands-on experience with Gong Forecasting configuration, dashboard development, and driving platform rollouts across an AE organization Required
Snowflake: proficiency querying and building on Snowflake as a data warehouse; familiarity with Snowflake AI / Cortex features Required
Salesforce: deep understanding of the Salesforce data model including Opportunity objects, pipeline mechanics, and Opportunity Splits Required
SQL: strong SQL skills for pipeline validation, ad hoc analysis, and data quality debugging Required
AI-first mindset: demonstrated experience evaluating and deploying AI tools within analytics workflows, with a clear point of view on where AI creates leverage in GTM contexts Required
Executive communication: ability to translate complex dat ... (truncated, view full listing at source)
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