Data Platform Engineer — Real-Time, Scalable Infrastructure

Webhosting; New York, Northern (NY, KY); Hybrid; USD 130,000 - 200,000; Full time

Job summary

Vercel is seeking a Data Platform Engineer to join our Data Engineering team and build the core infrastructure that powers data across the company. This role focuses on the platform itself—the streaming systems, storage layers, and tooling that engineers, analysts, and data scientists rely on daily.

You will work with Kafka, ClickHouse, Tinybird, and Snowflake to ensure throughput, reliability, and scalability while building self-service capabilities for data consumers across the org.

Qualifications

• Experience building data platforms and pipelines.
• Ability to design scalable streaming systems.
• Familiarity with observability and monitoring practices.

Responsibilities

• Contribute to the systems that ingest, move, and serve data across Vercel, building reusable infrastructure for multiple teams.
• Work with Kafka and streaming tooling to support real-time data movement under senior guidance.
• Help build monitoring, alerting, and observability to catch issues before outages.
• Contribute to internal tools and abstractions across ClickHouse, Tinybird, Snowflake for self-service access.
• Develop platform features with growing independence and participate in code reviews.
• Help implement data access controls, protection, and compliance with senior engineers.
• Get exposure to architecture decisions and analytics/AI/ML workloads at scale.

Skills

Kafka experience

Streaming data

Cloud data platforms

Observability

Data security

Tools

Kafka

ClickHouse

Tinybird

Snowflake

Job description

Vercel is seeking a Data Platform Engineer to join our Data Engineering team and build the core infrastructure that powers data across the company. This role focuses on the platform itself—the streaming systems, storage layers, and tooling that engineers, analysts, and data scientists rely on daily.

You will work with Kafka, ClickHouse, Tinybird, and Snowflake to ensure throughput, reliability, and scalability while building self-service capabilities for data consumers across the org.