Data Engineer & Architect

We're building the data infrastructure that makes AI agents trustworthy instead of error-prone.

We provide continuously refreshed, verified B2B data for autonomous AI agents and GTM workflows.

We've tripled growth while maintaining 100% gross dollar retention and staying cashflow positive.

We power AI agents for Clay, Orbital, Dun & Bradstreet, and the next generation of AI GTM tools.

Our data platform is scaling rapidly, and we need experienced data engineers and architects who can design systems that handle massive data volumes reliably and efficiently.

This role exists to strengthen our core data infrastructure, improve scalability and correctness, and ensure our datasets remain accurate, fresh, and trusted as customer usage grows.

You’ll work on a platform where data quality, reliability, and architecture directly impact customer trust and retention.

Design, build, and maintain scalable data pipelines handling high-volume structured and semi-structured data.

Own data infrastructure end-to-end, from ingestion and storage through transformation, validation, and delivery.

Architect and optimize data systems using Snowflake, S3, and modern cloud data stacks.

Ensure data freshness, accuracy, and consistency across production systems.

Collaborate closely with backend, frontend, and AI engineers to support product and customer use cases.

Define and enforce best practices around data modeling, schema design, and data quality checks.

Continuously improve performance, cost efficiency, observability, and reliability of the data platform.

Help raise the overall data engineering bar as the company and datasets scale.

You have 5+ years of professional experience in data engineering and/or data architecture roles.

Strong fundamentals in data modeling, ETL/ELT design, and distributed data systems.

Hands-on experience with:

• Snowflake

• Cloud object storage (AWS S3 or equivalent)

• High-volume batch and/or streaming data pipelines

• SQL and data transformation frameworks

Solid understanding of:

• Data warehousing concepts

• Data reliability, validation, and observability

• Cloud infrastructure and cost-performance tradeoffs

Experience working with large-scale, frequently updated datasets.

Comfortable operating in a fast-moving, remote-first environment.

You take ownership, communicate clearly, and design systems meant to last.

Preferred (not required):

• Familiarity with B2B data, GTM data, or enrichment pipelines

• Experience supporting AI/ML or agent-driven workflows

Product with real traction: Customers rely on our platform in production.

High ownership: Small team where your work directly shapes the product.

Engineering-driven culture: Quality and correctness matter.

Growth stage company: Clear product-market fit and momentum.

Impact over process: Less bureaucracy, more building.

Competitive compensation based on experience.

Meaningful ownership and long-term growth opportunities.

Flexible working hours.

Fully remote-friendly team.

Direct collaboration with founders and core engineering leadership.