Data Engineer

• 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, Zoominfo, Dun & Bradstreet, and the next generation of AI GTM tools.

• Our data platform is scaling rapidly, and we need engineer who can own pipelines end-to-end, keep data quality high, and ensure reliability as we grow.

• This role exists to strengthen our data infrastructure, accelerate delivery through automation, and ensure our B2B customers receive accurate, timely data they can trust.

• You'll work on data systems that directly power customer workflows - where pipeline reliability and data quality directly impact retention.

• Build and maintain production-ready data pipelines using DBT, Snowflake, and modern orchestration tools.

• Own data engineering features end-to-end, from implementation through optimization and deployment.

• Fix and improve existing pipelines - identify bottlenecks, resolve issues, and enhance performance.

• Drive automation initiatives across the data stack to accelerate delivery and reduce manual interventions.

• Provide 2nd line support for B2B customers - investigate data issues, clarify edge cases, and ensure customers can trust their data.

• Design and implement new data import pipelines as we expand our data source coverage.

• Implement data quality improvements - validation, monitoring, and testing to ensure reliable, accurate data delivery.

• Contribute to code reviews, architectural discussions, and data engineering best practices.

• You have 3+ years of professional data engineering experience.

• Strong fundamentals in SQL, data modeling, Python and ETL/ELT principles.

• DBT - hands-on experience building and maintaining transformation pipelines

• Snowflake
• Databricks
• AWS (S3, Lambda, Glue, etc.)

• Prefect or similar orchestration tools (Airflow, Dagster)

• Solid understanding of data quality principles, testing strategies, and monitoring practices.

• Comfortable working in a fast-moving, remote-first environment.

• Strong communicator - able to explain technical issues clearly to both technical and non-technical stakeholders.

• Async-first mindset - can work independently, document decisions, and keep stakeholders informed without constant synchronous communication.

• End-to-end ownership mentality - you see tasks through from planning to production, handling blockers and follow-through.

• You care about data quality, pipeline reliability, and long-term maintainability.

• 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.

Originally posted on Himalayas