Member of the Technical Staff, GTM

<div class="content-intro"><h2>About Vercel:</h2> <p><span data-sheets-root="1">Vercel is the agentic infrastructure company. We free people and agents to ship what’s next.</span></p> <p><span data-sheets-root="1">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.</span></p> <p><span data-sheets-root="1">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.</span></p> <p><span data-sheets-root="1">We are building the platform for that future, trusted by companies like&nbsp;<strong>OpenAI, PayPal, Ramp, Supreme, and millions of developers worldwide</strong>. Whether you’re building our products, supporting our customers, growing our community, or shaping our story, you’ll help define what comes next.</span></p></div><p><strong>About the Role:</strong></p> <p>We're looking for a Senior Data Engineer to join our Data team and own the reliability of the data our Finance stakeholders depend on. Vercel's revenue model combines subscription tiers with usage-based billing across many metered products, plus Enterprise contracts, so this data is genuinely complex to get right. This is a full-stack role: you'll build and maintain the pipelines, transformations, and data models that turn billing and usage data into the revenue, forecasting, and reporting infrastructure Finance runs on.</p> <p>You'll work closely with Finance 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 financial data infrastructure.</p> <p>If you want your work to directly shape how the business understands its revenue and financial performance, we'd love to hear from you.</p> <p><strong>What You Will Do:</strong></p> <ul> <li>Own pipelines that bring billing, usage, and contract data into the warehouse reliably, including metered usage across multiple products.</li> <li>Diagnose and resolve data quality and freshness issues at the source, not just downstream.</li> <li>Design and maintain dbt models that turn raw billing and usage data into clean, trusted datasets for revenue recognition, margin, and forecasting.</li> <li>Set testing and documentation standards so models hold up to the accuracy bar Finance requires when reconciling usage-based revenue against contracts.</li> <li>Build datasets and semantic models that power the dashboards and reports Finance leadership uses for planning, forecasting, and close.</li> <li>Reduce reliance on one-off requests by designing for self-service.</li> <li>Work with Finance leaders (FP&amp;A, Accounting, Revenue) to understand what they need from the data and why, and push back when the ask doesn't match the underlying question.</li> <li>Build pipeline and transformation code to a high engineering bar, and hold others to it through code review.</li> <li>Mentor other engineers and help set technical standards for the team.</li> </ul> <p><strong>About You:</strong></p> <ul> <li>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</li> <li>Strong SQL and Python skills, with experience writing production-grade, testable code, not just scripts for one-off analysis</li> <li>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)</li> <li>Direct experience with Finance data, ideally including usage-based billing, revenue recognition, or financial close/forecasting metrics, with genuine fluency in how those metrics are defined and used</li> <li>Proven ability to partner with non-technical stakeholders, translating ambiguous business questions into technical specs and durable data models, not just taking requirements at face value</li> <li>Strong communication skills, including presenting technical trade-offs to both technical and business audiences</li> <li>A track record of technical ownership, with the judgment to make architecture decisions independently and