Platform Engineer (Rust)

You will be working on fundamental components of both the Polars Cloud platform and the Polars distributed query engine. Some of the things you will be working on are designing gRPC / REST api's, leader election and failover for cluster nodes, replication of distributed query state and more. For these problems, you will cooperate closely with the cloud platform and compute engine teams. * You are familiar with Linux and can find what you need in manpages

• You are proficient in async Rust (Tokio)
• You are familiar with distributed systems architecture and implementation
• You prefer simple solutions to complex ones, but know when complex solutions are needed
• You enjoy working across different domains in a small team

Do you have experience in gRPC?

• An important role in a high-growth startup allowing plenty of growth opportunities in diverse directions
• Chance to work with and learn from a small strong technical team passionate about data processing and software engineering
• Competitive salary
• Stock option package
• Flexible working hours

We're looking for someone who can join us in our Amsterdam office twice a week to collaborate with the team, drive faster development and ideation, and help build our team culture.

Polars is the next generation of DataFrames. We don't take the easy route and build from the ground up, controlling every element allows us to create a superior solution. Our open-source implementation is one of the fastest growing data projects on GitHub and has over 30 thousand stars and 100 million downloads to date. We believe data processing should be a seamless experience from working locally on your development machine to an enterprise production environment. There should be one framework to learn for data practitioners, removing the need for rewriting code when going to production. To make that possible, we are building a cloud platform to seamlessly bring locally developed Polars pipelines to production. We are also building a distributed engine for Polars to scale beyond the limits of a single machine, both in the cloud and on-premises.