Responsibilities
- 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.
Requirements
- 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.
Nice to Have
- Familiarity with B2B data, GTM data, or enrichment pipelines
- Experience supporting AI/ML or agent-driven workflows
Benefits
- 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.
