Platform Engineer (Data Infrastructure)
Resumo da vaga pela JobGrid
Platform Engineer (Data Infrastructure) at Humbility: Vilnius, Lituânia; Presencial; Tempo inteiro; TI; Engenharia de dados. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: Vilnius, Lituânia, Presencial
- Role classification: TI, Engenharia de dados, Tempo inteiro
- Source freshness: checked by JobGrid on 2026-05-28.
- Application path: candidates continue to the employer application page with non-personal referral tags.
Humbility is an algorithmic cryptocurrency trading firm operating across DeFi and CeFi on multiple chains and exchanges. We build low-latency, market-neutral arbitrage systems in a highly competitive environment.
Our team of 50+ Software Engineers and Data Analysts thrives on solving mathematical and logical challenges at the cutting edge. As a private capital company without external clients, we have the freedom to take calculated risks, invest in emerging technologies, and pursue ambitious technical directions that client-facing organizations cannot. We build for scale, speed, and reliability—not to minimize costs.
We are hiring a Platform Engineer to bridge our Data Engineering and Platform Infrastructure teams, building the foundation that enables us to process and analyze massive volumes of trading data. Join us!
The scale you'll work with:
- Hundreds of terabytes of analytics data managed and archived
- 500k+ Kafka messages per second streamed
- Tens of regions globally
- On-premises hardware that cloud providers don't have yet—we deploy latest-generation infrastructure before it becomes mainstream
- ClickHouse, Iceberg, Kafka, Ceph, Kubernetes, Spark, PostgreSQL—and the tools you'll bring to the table
In this position, you will:
- Operate, configure, and scale our data infrastructure tooling to meet evolving platform needs
- Own the architecture of our data platform, solving the complex scalability challenges that come with operating at this scale across dozens of regions
- Design and implement systems for scalability and reliability as strategy count and data volumes expand
- Write and maintain tooling to troubleshoot and monitor data ingestion pipelines
- Enable strategy teams to ingest, process, and archive vast amounts of trading and market data
- Develop runbooks and continuously improve automation to reduce operational overhead
- Drive innovation in our data stack—we build for the future, not to maintain legacy systems