Data Engineer
LO:TECH is a capital markets firm for the onchain world, providing digital-asset market making, institutional-grade market data, analytics, and execution services. It serves institutional clients, trading firms, funds, banks, researchers, and token projects.
About LO:TECH
LO:TECH is a London-based trading business focused on onchain and digital-asset capital markets. Its proprietary high-frequency trading technology and infrastructure support liquidity provision across centralized and decentralized venues, agency execution algorithms, and institutional market-data services. The company provides live and historical tick-level crypto and prediction-market data through APIs, CSVs, websocket streams, and replay services, alongside transparent market-making dashboards and tailored enterprise data plans. Its clients include token projects, quantitative researchers, funds, banks, and other institutional market participants.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Own pipelines that turn raw centralized and decentralized exchange feeds into reliable market data, including ingestion, normalization, reconciliation, storage, APIs, feeds, service levels, monitoring, and performance-critical processing.
Requirements
- 2–4 years building production data infrastructure
- Strong Python and Rust skills, particularly Polars
- AWS and command-line experience
- Strong SQL and data-quality expertise
- Advanced degree in mathematics, statistics, physics, computer science, engineering, or a similar field
- Enthusiasm for systematic research and market microstructure
- Prior high-frequency trading experience
- Sharp, curious, low-ego working style
Responsibilities
- Build and maintain data ingestion from CEX and DEX venues using WebSocket, REST, and on-chain feeds
- Normalize, validate, and reconcile market data, including gap detection, deduplication, and schema drift handling
- Own storage and delivery for time-series and historical datasets, APIs, feeds, and SLAs
- Build monitoring and alerting for data quality
- Maintain fast performance-critical data paths