Finance Data Engineer: Revenue, Forecasting & Pipelines

Vercel; United States; Hybrid; USD 170,000 - 260,000; Full time

Benefits offered by this job

Competitive compensation & equity

Inclusive healthcare package

Mentorship & events

Job summary

Vercel is seeking a software engineer to join the Data team and own the reliability of data used by Finance stakeholders. This full-stack role builds pipelines, transformations, and data models turning billing and usage data into revenue, forecasting, and reporting infrastructure.

You will partner with FP&A, Accounting, and Revenue to ensure metrics are defined and trusted, while raising the bar for data quality and engineering standards within the team.

Qualifications

• 4+ years of experience in data engineering, analytics engineering, or a closely related field.
• Proven ability to own production data pipelines end-to-end.

Responsibilities

• Own pipelines that bring billing, usage, and contract data into the warehouse reliably.
• Diagnose and resolve data quality and freshness issues at the source.
• Design and maintain dbt models turning raw data into clean datasets for revenue recognition, margin, and forecasting.
• Set testing and documentation standards for accuracy in Finance reconciliations.
• Build datasets and semantic models for dashboards and reports used by Finance leadership.
• Reduce reliance on one-off requests by designing for self-service.
• Collaborate with Finance leaders to align data with business questions.
• Mentor engineers and set technical standards for the team.

Skills

SQL

Python

Data modeling

Stakeholder communication

Production-grade code

Tools

dbt

Airflow

Dagster

Job description

Vercel is seeking a software engineer to join the Data team and own the reliability of data used by Finance stakeholders. This full-stack role builds pipelines, transformations, and data models turning billing and usage data into revenue, forecasting, and reporting infrastructure.

You will partner with FP&A, Accounting, and Revenue to ensure metrics are defined and trusted, while raising the bar for data quality and engineering standards within the team.