Transformation & data modeling

• 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

Semantic layer & metric definitions

• 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

AI-assisted reporting & measurement

• 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

Governance & controls

• Apply access controls, data classification, and change management suited to financial and customer data in SOX, SOC 1, and SOC 2 scope

What success looks like

• 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-assisted reporting live

Required Qualifications

• 5+ years in analytics engineering, data engineering, or business intelligence, including at least 2 years owning dbt models in production
• Expert SQL: window functions, CTEs, performance tuning, and the ability to work through legacy queries you didn't write
• Hands-on dbt experience across models, tests, documentation, incremental models, and CI
• Experience with a semantic layer tool (dbt Semantic Layer/MetricFlow, LookML, Cube, or similar)
• Strong understanding of recurring revenue metrics (ARR, CARR, bookings, backlog, churn) and how they tie to billing and revenue recognition
• Practical data modeling skills: dimensional modeling, slowly changing dimensions, and explicit grain
• Experience working with Salesforce and NetSuite data
• Experience with a cloud data warehouse (Redshift, Snowflake, BigQuery, or similar)
• Regular use of AI coding and data tools (e.g., Claude) in your own workflow
• Comfort working directly with US-based Finance and business stakeholders, with a few hours of overlap with US time zones

Preferred Qualifications

• Python for transformation logic, automation, or API-based extraction
• Experience with BI tools such as Looker, Tableau, Power BI, or Sigma
• Exposure to fund administration, private markets, or financial services
• Experience building AI-ready data products or metrics that agents can query
• A governance instinct: you understand why a change to revenue or customer data needs a control owner and a ticket

• Health, dental, and vision care for you and your family
• Life insurance
• Mental wellness coverage
• Fertility and growing family support
• Flex Time Off in addition to company-paid holidays
• Paid family leave, medical leave, and bereavement leave policies
• Retirement savings plans
• Allowance to customize your work and technology setup at home
• Annual professional development stipend

Your recruiter can provide additional details about compensation and benefits.