Senior AI Data Analyst - Sales

Key Responsibilities Executive & Operational Analytics Owns dashboards: pipeline generation, coverage, forecast, win rates, sales cycle, quota attainment, ARR, net-new vs. expansion Builds Pigment models and opportunity-level datasets for pipeline, capacity, revenue Standardizes metrics/definitions across teams and regions Planning & Capacity Modeling Analytics backbone for annual and in-year sales planning cycles Builds/maintains capacity, quota, productivity models in Pigment Models headcount and territory scenarios; runs what-if analysis against historical performance Data Architecture & Automation Designs data pipelines from Salesforce, Pigment, Gong, Netsuite, etc.

Partners with Data Engineering on dbt/warehouse transformations Builds AI skills on Pigment MCP for recurring reports and event-driven alerts Owns data quality, governance, documentation Business Partnership Analytics partner to Sales leadership and regional orgs Answers questions like: what's driving win rate/cycle changes, where is coverage weakest, how does pipeline quality Senior AI Data Analyst - GTM AI & Analytics Team Owns the data strategy powering the global Sales org - pipeline health, forecasting, sales planning (capacity, quota, territory, headcount), and pipeline/revenue growth across enterprise accounts.

Sits between analytics, data engineering, and business strategy; partners with Sales leadership, RevOps, Marketing, and Finance. AI-Native Component Contributes to GTM Cortex, the company's GTM analytics/orchestration layer: Builds analytical "skills" as version-controlled code (atomic to composite), tested and maintained in GitHub Works in Claude Code, querying live Pigment data through the Pigment MCP References a central KPI Catalog rather than redefining metrics ad hoc Converts recurring sales questions into reusable skills instead of one-off analyses Drives adoption of Analyst/Modeler AI agents across Sales Key Responsibilities Executive & Operational Analytics Owns dashboards: pipeline generation, coverage, forecast, win rates, sales cycle, quota attainment, ARR, net-new vs. expansion Builds Pigment models and opportunity-level datasets for pipeline, capacity, revenue Standardizes metrics/definitions across teams and regions Planning & Capacity Modeling Analytics backbone for annual and in-year sales planning cycles Builds/maintains capacity, quota, productivity models in Pigment Models headcount and territory scenarios; runs what-if analysis against historical performance Data Architecture & Automation Designs data pipelines from Salesforce, Pigment, Gong, Netsuite, etc.

Partners with Data Engineering on dbt/warehouse transformations Builds AI skills on Pigment MCP for recurring reports and event-driven alerts Owns data quality, governance, documentation Business Partnership Analytics partner to Sales leadership and regional orgs Answers questions like: what's driving win rate/cycle changes, where is coverage weakest, how does pipeline quality Enablement Builds role-based reporting for reps, managers, execs Trains Sales teams on data use in pipeline reviews, QBRs, forecast calls Drives adoption of AI-driven workflows Required Qualifications 5-8+ years in Data Analytics, Analytics Engineering, or BI Advanced SQL; experience with Snowflake/BigQuery/Redshift Strong SaaS/sales metrics background (ARR, pipeline gen, coverage, win rate, quota attainment, forecast accuracy) Hands-on BI/planning tools (Pigment, Looker, Tableau, Power BI, Mode) Experience supporting enterprise Sales/RevOps, including GTM planning Genuine interest in AI-native analytics (LLM-assisted analysis, skills-as-code, governed data access) Nice to Have Pigment experience Salesforce, Gong, Clari, Outreach/Salesloft, Segment, or Amplitude Claude Code or MCP-based data access experience dbt, Python, or analytics engineering background B2B enterprise SaaS experience Familiarity with complex, multi-segment sales/forecasting motions Success Metrics Sales leadership relies on insights for pipeline, forecast, capacity planning Risks surfaced early, not just reported Dashboards/AI skills used in every pipeline review and QBR Growing library of reusable analytics skills reduces one-off requests Planning cycles run on these models Data is trusted, consistent, embedded in workflows We are targeting a total comp package of $160kOTE for this role We conduct background checks as part of our hiring process, in accordance with applicable laws and regulations in the countries where we operate.

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