Senior Data Engineer - GTM

Vercel is the agentic infrastructure company. We free people and agents to ship what’s next.

For more than a decade, Vercel has shaped how the web is built. As the team behind Next.js, v0, and AI SDK, we create products that help builders move from idea to production with speed, security, and exceptional developer experience.

Now, software is entering a new era, and the next generation of products will not just be used by people. They will be built, extended, and operated by agents.

We are building the platform for that future, trusted by companies like OpenAI, PayPal, Ramp, Supreme, and millions of developers worldwide. Whether you’re building our products, supporting our customers, growing our community, or shaping our story, you’ll help define what comes next.

We're looking for a Senior Data Engineer to join our Data team and own the reliability of the data our GTM stakeholders depend on. This is a full-stack role: you'll build and maintain the pipelines, transformations, and data models that turn CRM, marketing, and product usage data into the reporting and forecasting infrastructure that Sales, Marketing, and RevOps run on.

You'll work closely with GTM stakeholders to understand how metrics are used and make sure they're right, not just build to spec. Reporting to the Data team's engineering lead, you'll have full ownership over your area, set technical direction, and help raise the bar for how the team builds GTM data infrastructure.

If you want your work to directly shape how the business understands its pipeline, revenue, and go-to-market performance, we'd love to hear from you.

• →Own pipelines that bring GTM source systems, including CRM, marketing automation, outbound tooling, and product usage, into the warehouse reliably.
• →Diagnose and resolve data quality and freshness issues at the source, not just downstream.
• →Design and maintain dbt models that turn raw GTM data into clean, trusted datasets, including pipeline, revenue, attribution, and funnel metrics.
• →Set testing and documentation standards so models are trustworthy and easy for others to extend.
• →Build datasets and semantic models that power the dashboards and reports Sales, Marketing, and RevOps leadership run on.
• →Reduce reliance on one-off requests by designing for self-service.
• →Work with Sales, Marketing, and RevOps leaders to understand what they need from the data and why, and push back when the ask doesn't match the underlying question.
• →Bring enough context on how the business runs its GTM motion that you can spot bad metrics before they ship.
• →Build pipeline and transformation code to a high engineering bar, and hold others to it through code review.
• →Form and advocate for a point of view on GTM data modeling and pipeline design.
• →Mentor other engineers and help set technical standards for the team.

• 4+ years of experience in data engineering, analytics engineering, or a closely related field, with a track record of owning production data pipelines end-to-end
• Strong SQL and Python skills, with experience writing production-grade, testable code, not just scripts for one-off analysis
• Hands-on experience with dbt (or a comparable transformation framework), dimensional/data modeling, and modern ELT/ETL workflows, including orchestration tooling (e.g., Airflow, Dagster)
• Direct experience with GTM data, e.g. CRM data models, pipeline and revenue reporting, sales/marketing attribution, and genuine fluency in how those metrics are defined and used
• Proven ability to partner with non-technical stakeholders (Sales, Marketing, RevOps leadership), translating ambiguous business questions into technical specs and durable data models, not just taking requirements at face value
• Strong communication skills, including experience presenting technical trade-offs to both technical and business audiences and driving alignment across teams with competing priorities
• A track record of technical ownership, with the judgment to make architecture and modeling decisions independently, and interest in raising the bar for a team's data practices, including mentoring more junior engineers