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Overview
Join Vercel as a Senior Data Engineer to own the reliability of data for GTM stakeholders. Build and maintain data pipelines and models, ensuring data quality and supporting business decisions. About Vercel: 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.
What You Will Do
- •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.
Benefits
- •Competitive compensation package, including equity.
- •Inclusive Healthcare Package.
- •Learn and Grow - we provide mentorship and send you to events that help you build your network and skills.
- •Flexible Time Off.
- •We will provide you the gear you need to do your role, and a WFH budget for you to outfit your space as needed.
- •The San Francisco, CA base pay range for this role is $170,000 - $260,000. Actual salary will be based on job-related skills, experience, and location. Compensation outside of San Francisco may be adjusted based on employee location. The total compensation package may include benefits, equity-based compensation, and eligibility for a company bonus or variable pay program depending on the role.
About You
- •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.