Employer description & source details
Own dbt models for revenue and customer data sourced from CRM, CPQ, billing, and ERP systems Model the chain from quote to order to provisioning to invoice to recognized revenue at the entity and service grain Move reporting-level data generation out of Salesforce and into the warehouse, where other systems can consume it Define and govern certified metrics like ARR and CARR and the dimensions behind them Write definitions precise enough for both people and AI agents to reason over consistently, backed by documentation, tests, and lineage Reconcile metrics with NetSuite and the Finance close so the numbers tie out Translate business questions from Sales, Finance, Fund Administration, and leadership into technical requirements for Data Engineering, covering source data, grain, freshness, and quality Work with our Lead Business Systems Analyst and Integrations Engineers so the commercial model and source data are reportable from the start Build an AI-assisted reporting layer on top of governed semantic definitions, so questions asked through tools like Claude return certified answers Build self-serve dashboards for revenue, operations, and leadership stakeholders Stand up measurement for the Lead-to-Cash program so monthly progress is shown in numbers rather than status updates Apply access controls, data classification, and change management suited to financial and customer data in SOX, SOC 1, and SOC 2 scope First 90 days: own the core revenue dbt models, and publish draft definitions for ARR, CARR, and backlog reviewed with Finance Within 6 months: a certified semantic layer for revenue metrics, and the first in-service ARR figure computed from systems Within 12 months: one number of record for each revenue metric, used across Finance, Sales, and leadership, with AI-as
Extracted skills: python, sql, power bi, tableau, sales, finance, operations