ParadeDB (YC S23) Is Hiring Database Engineers

https://news.ycombinator.com/rss Hits: 12
Summary

What we do ParadeDB is a transactional alternative to Elasticsearch built on Postgres. We build state-of-the-art full-text search and columnar analytics as a Postgres extension. Companies like Modern Treasury, BILT Rewards, Alibaba Cloud, and UnifyGTM use ParadeDB to: Eliminate ETL/change data capture tools Add transactional guarantees to their search indexes Perform JOINs on their search data We're a small, focused team of engineers dedicated to creating an inclusive and enjoyable work environment. We have an office in San Francisco, with some team members distributed across the United States and Canada. We’re a Series A Company with a flat structure, strong investors, and lots of room for growth. What we want We're looking for an OLAP internals database engineer to join our team full-time. The ideal candidate should have: A solid foundation in data systems/internals Experience building analytical query engines, from columnar storage to query optimization (bonus points if you were responsible for building vectorized processing optimizations) Proficiency in systems programming with C/C++/Rust/Zig (bonus points for Rust) Strong background in full-text search systems and familiarity with Tantivy is a plus Familiarity with Postgres internals and pgrx is a plus We're seeking someone creative, independent, and excited by difficult technical challenges who can teach us as much as they learn from us. The ideal candidate will bring thoughtful opinions to the team, be excited to engage with and learn from customers, and be willing to wear many hats. What you want You want to be part of the core team of a fast-growing infrastructure startup You want to wear many hats and interface directly with customers You’re passionate about the OLAP ecosystem, Postgres, databases, and/or search technologies You’re excited about diving deep into the internals of Postgres and writing high-performance, safe Rust code to elevate Postgres for search and analytical workloads

First seen: 2026-01-03 14:17

Last seen: 2026-01-04 01:19