Senior Data Engineer
Hyperbolic Labs1 month ago
Responsibilities
- Build and maintain a data warehouse that ingests and aggregates data from multiple sources.
- Own data pipelines and the tooling that keeps the data infrastructure healthy.
- Design data models for new business lines, including spot and inference products.
- Ensure data reliability and quality through testing, documentation, and governance.
- Support production data systems through monitoring, troubleshooting, and performance tuning.
- Partner with data analysts and engineering to make data reliable, fast, and cost-efficient to use.
- Evaluate requests and push back on approaches that would create technical debt.
Requirements
- Track record of independently building and maintaining a data warehouse using industry-standard tooling.
- Experience designing data models for new products and business lines.
- Experience ensuring data reliability and quality through testing, documentation, and governance.
- Familiarity with data lakes and data warehouses and when to use each.
- Deep knowledge of Postgres, including partitioning, data archival, and moving large datasets.
- Experience working with large datasets and supporting production systems.
- Strong communication skills and sound technical judgment.
Benefits
- Work with a high degree of autonomy as Hyperbolic Labs’ first dedicated data engineer.
- Work alongside a two-person data team with a direct line to impact.
- Equal opportunity employer committed to diversity and an inclusive environment.
Tech Stack
Categories
Data Engineering
About Hyperbolic Labs
Hyperbolic Labs builds an open-access AI cloud that provides on-demand GPU clusters, a GPU marketplace for idle compute, and managed inference for AI startups, ML teams, and researchers. It sells capacity and services for training and serving models at production scale. The company is privately held, headquartered in San Francisco, and raised a Series A in 2024.