Senior Data Engineer

About the role

WHO WE ARE

Join us in putting the world’s compute behind the people building what comes next.

At Hyperbolic, we’re making global AI compute liquid. We bring fragmented, heterogeneous GPU capacity together into one unified, productive platform, making it easier to access, sell, resell, manage, and orchestrate the resources that power AI. For neocloud, we turn GPU capacity into productive infrastructure that delivers lasting value and accelerate their time to market.

We believe bold ideas deserve the computing power to become reality. We’re building toward a future where compute flows as freely as electricity, available when and where it’s needed, so builders everywhere can turn their most ambitious ideas into breakthroughs.

We’re looking for curious, driven people who take ownership, challenge assumptions, and care deeply about what they build. If you’re energized by hard problems and want to help shape how the world accesses AI, we’d love to build with you.

ABOUT THE ROLE

This is Hyperbolic's first dedicated Data Engineer, and you will build our data infrastructure from 0 to 1. You'll be one half of a two-person data team, working alongside our data analyst, which means a high degree of autonomy and a direct line to impact. Today our product engineers maintain the data stack on top of their day jobs. You will take ownership of the data warehouse, the pipelines that feed it, and the tooling that keeps it healthy.

The timing matters. As we launch new products like inference, our data volume will grow fast, we will add more data sources, and more teams will rely on BI and reporting. Scrappy processes and off-the-shelf tools won't keep up at that scale, and you will build what comes next.

You will sit between our data analysts and engineering, partnering with both to make sure the right data is reliable, fast, and cost-efficient to use.

Our current data stack includes Redshift, Postgres, and Hex, with Polytomic for ingestion.

WHO YOU ARE

- Track record of independently building and maintaining a data warehouse that ingests and aggregates data from multiple sources using industry-standard tooling

- Experience designing data models for new business lines, figuring out what to model and how as new products (like spot and inference) come online

- 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

- Experience supporting production systems: monitoring, troubleshooting, and performance tuning

- Strong communication skills, with the judgment to push back on requests that would add tech debt and explain why

PREFERRED QUALIFICATIONS

- Familiarity with the AWS ecosystem (this can be learned on the job)