Senior Analytics Engineer, GTM Analytics

At Vanta, our mission is to help businesses earn and prove trust. We believe that security should be monitored and verified continuously, and we empower companies to practice better security and prove it with ease. Vanta has a kind and talented team, and while some have prior security experience, many have been successful at Vanta without it. 

As GTM’s first Analytics Engineer, you will own and define the analytics data architecture that powers Vanta’s go-to-market organization. This is a highly strategic role responsible not just for building data models, but for establishing the foundation, standards, and governance that ensure consistent, trusted metrics across GTM, Finance, and RevOps.

Embedded within the GTM Analytics team, you will lead the design of Vanta’s revenue data model and semantic layer—shaping how core business metrics are defined, calculated, and consumed across the company. You’ll partner closely with business-facing analytics teams to align on definitions and logic, drive adoption of standardized reporting, and influence the evolution of our data platform to support scalable analytics and AI-driven insights.

This role sits at the intersection of data architecture, analytics engineering, and business strategy. Your work will directly impact how Vanta makes revenue decisions by enabling a single source of truth and laying the groundwork for next-generation analytics.

What you’ll do as a Senior Analytics Engineer at Vanta:

• Own GTM Analytics Architecture: Define how core revenue metrics (ARR, NRR, GRR, pipeline, bookings, quota) are modeled, standardized, and surfaced across the organization.

• Act as an Analytics Engineering Thought Leader: Influence how GTM teams define, consume, and trust data by promoting best practices, shaping data culture, and guiding stakeholders toward consistent, scalable approaches.

• Define & Govern the Semantic Layer: Build and maintain a version-controlled semantic layer that serves as the single source of truth for metric definitions.

• Partner with Business-Facing Analytics Teams: Partner with GTM Analytics, Finance, and RevOps to translate business definitions into scalable data models, resolve ambiguity in logic, and ensure consistent metric interpretation across stakeholders.

• Drive Metric Alignment & Governance: Establish and operationalize processes to align on KPI definitions, enforce consistency, and reduce fragmentation in reporting across teams.

• Lead Scalable Data Modeling: Design and implement reusable dbt models and data marts that are accurate, auditable, and fully reconciled with Finance.

• Set Analytics Engineering Standards: Define best practices for dbt development, testing, and documentation to ensure quality and scalability.

• Influence Platform & Analytics Roadmap: Partner with Data Engineering to improve data reliability, performance, and capabilities that support advanced analytics and AI use cases.

How to be successful in this role:

• Experience: 7–10+ years in Analytics Engineering, Data Engineering, or similar roles, with a track record of owning data architecture and driving cross-functional alignment on metrics and reporting

• dbt & Data Modeling Expertise: Deep experience with dbt and dimensional modeling in modern warehouses (Snowflake, BigQuery, Redshift).

• GTM/Revenue Metrics: Strong understanding of revenue metrics (ARR, NRR, GRR, pipeline, ACV, quota) and how they’re used across GTM and Finance.

• SQL Mastery: Advanced SQL skills for building scalable, performant transformations

• Collaboration: Proven ability to work effectively with cross-functional partners, including business analysts, finance teams, data engineers, and business stakeholders. You can translate technical concepts for non-technical audiences and vice versa.

• Data Quality & Governance: Strong data quality mindset - you proactively implement testing, reconciliation processes, and data quality checks to prevent issues before they impact stakeholders.