About the Company:
We’re working with a technology-first hedge fund where technology is the core of the business.
The company’s key assets include:
• Trading infrastructure for both crypto and traditional markets
• AI models and research pipelinesused directly in trading decision-making
• Next-generation computing systems, including experiments with novel hardware and performance-critical software
The company builds trading systems at the intersection of financial markets, AI, and frontier technologies. The fund operates its own AI research lab, has stable long-term funding, and access to experimental hardware. At the current stage, the team is entering a phase where research is being reinforced by engineering and trading, including the development of high-frequency trading (HFT) systems with strict latency and reliability requirements.
The company’s key assets include:
• Trading infrastructure for both crypto and traditional markets
• AI models and research pipelinesused directly in trading decision-making
• Next-generation computing systems, including experiments with novel hardware and performance-critical software
The company builds trading systems at the intersection of financial markets, AI, and frontier technologies. The fund operates its own AI research lab, has stable long-term funding, and access to experimental hardware. At the current stage, the team is entering a phase where research is being reinforced by engineering and trading, including the development of high-frequency trading (HFT) systems with strict latency and reliability requirements.
About the Role:
The team is looking for a Data Engineer to build and operate the data platform that powers company’s researchers, AI systems, and trading infrastructure. You will own the lifecycle of market, execution, alternative, and internal research data - from ingestion and validation to storage, enrichment, and delivery. This role sits at the intersection of distributed systems, quantitative research, and machine learning. Your work will directly impact how quickly new ideas can be tested, how reliably models can be trained, and how efficiently strategies can be deployed.
What You'll Do:
- Design and maintain scalable pipelines for market, trading, and research data.
- Build reliable ingestion systems for exchange feeds, execution venues, alternative datasets, and internal data sources.
- Develop data quality frameworks, validation checks, monitoring, and observability tooling.
- Create standardized datasets used across quantitative research, machine learning, and production trading systems.
- Optimize storage and retrieval of large-scale time series data.
- Work closely with researchers to transform raw data into research-ready features.
- Partner with engineering teams to ensure consistent data models across research and production environments.
- Improve metadata management, lineage tracking, and reproducibility of research results.
- Evaluate and integrate new datasets that may provide trading edge.
You might thrive in this role if you have:
- Experience working with market data, order book data, or trading systems.
- Experience supporting machine learning workflows.
- Knowledge of quantitative research and backtesting environments.
- Great software engineering skills in C++ or Python.
- Experience building production-grade data pipelines and distributed data systems.
- Deep understanding of data modeling, storage formats, and data lifecycle management.
- Good SQL skills and practical experience working with large datasets.
Location & Format:
- Hybrid work format in Amsterdam
- Full relocation support provided
Why this role is interesting?
- Long-term work at the intersection of trading, AI, and frontier technologies
- Opportunity to build systems from scratch
- Access to an in-house AI research lab
- Use of modern and experimental hardware
Send your CV on Telegram: @dariiyah