DeepFin is a systematic proprietary trading firm combining deep learning, traditional quantitative research methods, and cutting-edge trading technology, to trade global markets. Founded by engineers and researchers, we build and deploy advanced trading systems that operate across global markets.
Our team is lean, highly technical, and impact-driven - every hire plays a direct role in shaping the firm’s technology, strategy, and performance. We value curiosity, precision, and collaboration, and we’re building an environment where exceptional people can do their best work at the intersection of AI and financial markets.
Quantitative HFT Data Engineer
About DeepFin Research
DeepFin Research is a proprietary high-frequency trading (HFT) firm powered by cutting-edge Deep Learning (DL) and Deep Reinforcement Learning (DRL). We've brought on teammates from Nvidia, DeepMind, CitSec, Graviton, Tower, Jump, and others, and are aggressively working across cutting edge AI research and traditional quant research methods to monetise our AI generated signals across the global financial markets.
The Role
We are looking for exceptional quantitative data engineer to setup and manage data pipelines that are central to our cutting edge AI and quantitative research. This will involve deep understanding of L3 market data and its use in research contexts, as well as working with AWS, proprietary research clusters, custom databases, in house data pipelines, and expert-level Python.
Key Responsibilities
- L3 Data Pipelines: Architect ultra-high-throughput, zero-copy data pipelines to parse, normalize, and reconstruct nanosecond-precision Level 3 Market-By-Order (MBO) data from raw PCAP files and proprietary exchange protocols (e.g., ITCH).
- Storage & Schema Design: Implement highly compressed, column-oriented storage formats (e.g., Apache Arrow, Parquet, Zarr) optimized for massive time-series retrieval on distributed file systems and NVMe arrays.
- Data Quality & Integrity: Build robust, automated frameworks to detect anomalies, reconcile dropped packets, handle corporate actions, and ensure 100% data fidelity.
- High-Performance Tooling: Develop lightning-fast APIs and research libraries in Python and C++ (or Rust) allowing Quant Researchers and AI Scientists to query terabytes of tick data in seconds.
- Collaboration: Work side-by-side with AI researchers, quant strategists, and core execution engineers to ensure offline backtest environments perfectly simulate live market mechanics and latency constraints.
Required Experience & Skills
- 3–7+ years of Data Engineering experience, with a significant portion spent in quantitative finance, HFT, or a highly data-intensive AI research environment
- Exceptional Python and systems language skills: Expert-level Python (including internals and C-extensions), Apache Iceberg tables (S3 + Glue Catalog).
- Deep Microstructure Knowledge: You intimately understand the mechanics of limit order books, L2 vs. L3 data, trade matching engines, and the nuances of raw exchange feeds.
- Advanced Data Ecosystems: Deep hands-on experience with modern data tools and libraries such as Polars, Apache Arrow, NumPy, ClickHouse, or time-series specific databases (e.g., kdb+/q, Arctic).
If you’re passionate about applying advanced technology to real-world markets and want to work alongside a focused, high-performing team, we’d love to hear from you. DeepFin offers a collaborative, research-driven environment where ideas move quickly from concept to execution and where every contribution has visible impact.
Join us in building the next generation of deep-learning-driven trading systems - shaping the future of finance through innovation, rigour, and technology.
