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Salary: USD 264,000 - 396,000 / annual
Vercel is seeking a Member of Technical Staff to lead the design and development of the Financial Data Platform—the foundational system powering revenue reporting, consumption-based billing, financial forecasting, and compliance across the organization.
Reporting to the Senior Director of Data Engineering, you will define the technical vision for Vercel's financial data ecosystem, partnering closely with Finance, Accounting, and Revenue Operations leadership to translate business and compliance requirements into scalable systems. You'll architect reliable, auditable pipelines using modern technologies including Kafka, ClickHouse, Tinybird, and Snowflake, while writing production-grade code and mentoring engineers as the function grows.
Key responsibilities include:
- Designing and implementing a data platform supporting revenue recognition, consumption-based billing, and financial reporting across batch and real-time workloads, leveraging Kafka and streaming technologies to power real-time usage and billing pipelines.
- Working directly with FP&A, Accounting, and RevOps to understand reporting requirements, close-cycle timelines, and forecasting needs, ensuring data availability and accuracy align with financial reporting deadlines, audit cycles, and board-level reporting.
- Defining and maintaining guidelines for financial data ingestion, transformation, and storage with focus on auditability, lineage, and data integrity, overseeing data modeling, ETL, and warehousing across ClickHouse, Tinybird, and Snowflake.
- Developing end-to-end solutions across the finance data stack, setting the example for engineering excellence in a high-accountability domain, and mentoring engineers on technical craft and the rigor required when data feeds into financial statements.
- Partnering with Security, Compliance, Legal, and Internal Audit to ensure financial data pipelines meet SOX, SOC 2, and other regulatory requirements, championing high availability, fault tolerance, and auditability through comprehensive monitoring and incident response.
- Driving architectural decisions and roadmap planning that balance efficiency, scalability, and correctness requirements of financial systems, evaluating build-vs-buy tradeoffs across financial and billing tooling.
- Building data infrastructure that translates raw usage into revenue, supporting forecasting models Finance and GTM rely on for target-setting and reporting.
- Acting as a key decision-maker for evaluating and selecting data architectures and technologies critical to Vercel's financial operations, establishing a multi-phase roadmap for finance data platform maturity.
Requirements:
- 8+ years of experience in data engineering, analytics engineering, or closely related field, with a track record of owning production data pipelines end-to-end.
- Proven track record designing and operating large-scale data infrastructures in complex, fast-paced environments, with experience across Kafka and its ecosystem (Kafka Streams, Confluent Platform), ClickHouse, Tinybird, Snowflake, and broader big data frameworks.
- Prior experience building data pipelines that feed financial reporting, revenue recognition, billing, or consumption-based pricing models; familiarity with financial/ERP systems (NetSuite, Workday) or billing platforms is a strong plus.
- Proficiency in cloud platforms (AWS, GCP, or Azure) and associated big data services.
- Strong background in data governance, security, and compliance, including direct experience supporting audit, SOX, or SOC 2 requirements, and a rigorous approach to data lineage and accuracy.
- Outstanding communication and collaboration skills, with ability to translate financial and accounting requirements into technical architecture and influence both technical and non-technical stakeholders, including Finance and Audit leadership.
- Leadership mindset with demonstrated ability to mentor teams, drive consensus, and advocate for best practices in high-accountability data environments.
- Industry recognition or notable contributions in data engineering (published works, open-source contributions) is a plus.
