Senior Data Engineer - Finance

The shape of it

In the posting’s own words

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.

What it asks for · 8

• 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 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
• 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
• Strong communication skills, including presenting technical trade-offs to both technical and business audiences
• A track record of technical ownership, with the judgment to make architecture decisions independently and mentor other engineers
• Comfort with the accuracy and auditability standards Finance data requires (e.g., reconciliation, versioning, clear lineage)

What the job covers

• Own pipelines that bring billing, usage, and contract data into the warehouse reliably, including metered usage across multiple products.
• Diagnose and resolve data quality and freshness issues at the source, not just downstream.
• Design and maintain dbt models that turn raw billing and usage data into clean, trusted datasets for revenue recognition, margin, and forecasting.
• Set testing and documentation standards so models hold up to the accuracy bar Finance requires when reconciling usage-based revenue against contracts.
• Build datasets and semantic models that power the dashboards and reports Finance leadership uses for planning, forecasting, and close.
• Reduce reliance on one-off requests by designing for self-service.
• Work with Finance leaders (FP&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.
• Build pipeline and transformation code to a high engineering bar, and hold others to it through code review.
• Mentor other engineers and help set technical standards for the team.

Tools and skills named

Words the posting leans on

• data17×
• finance8×
• revenue7×
• models6×
• technical6×
• billing5×
• build5×
• engineering4×
• experience4×
• pipelines4×
• usage4×
• bar3×
• business3×
• code3×
• contracts3×
• engineers3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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