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Quant Data Engineer - Low Latency Trading Systems
Résumé du poste
A financial technology firm in Singapore seeks a Data Infrastructure Engineer to develop and maintain scalable data pipelines for high-frequency trading. The ideal candidate will have strong skills in Linux and C++, along with experience in Python for data orchestration. This role offers hands-on experience in a dynamic environment, working closely with quant researchers and AI engineers, with the potential to transition to full-time positions in Data or Systems Engineering.
Prestations
Qualifications
- Strong knowledge of Linux systems, shell scripting, and process management.
- Proficiency in C++ with experience in data structures and multithreading.
- Experience in Python, particularly with pandas and multiprocessing.
- Understanding of databases like ClickHouse and PostgreSQL.
- Basic familiarity with AI/ML workflows.
- Strong grasp of data engineering principles.
Responsabilités
- Build and maintain scalable data pipelines for high-frequency market data.
- Manage and optimize processes across Linux servers.
- Collaborate on integrating C++ modules into the data infrastructure.
- Implement tools for data ingestion and synchronization.
- Collaborate with AI engineers for multi-agent frameworks.
- Benchmark system performance and identify bottlenecks.
- Build health checks and diagnostic dashboards.
Connaissances
This role focuses on developing and maintaining scalable data infrastructure for high-frequency trading systems. You’ll design and automate pipelines to ingest, clean, and synchronize market data (minute, tick, L2) across C++ and Python layers, while managing performance on Linux servers.
Key skills include Linux systems engineering, Python (pandas, multiprocessing, FastAPI), C++ (I/O, threading), and database management (ClickHouse/PostgreSQL). Familiarity with AI/ML tools and multi-agent frameworks is a plus.
You’ll gain hands‑on experience with real quant infrastructure, performance optimization, and agentic AI systems, working closely with quant and AI engineers. Strong performers can transition into full‑time Data or Systems Engineer roles.
Data Infrastructure Development: Build and maintain scalable data pipelines for ingesting, transforming, and storing high-frequency market data (minute, tick, and L2).
Linux Systems Engineering: Manage and optimize processes across Linux servers (file systems, permissions, cron jobs, daemons, I/O optimization).
C++ Integration: Collaborate with developers to integrate C++ backtesting and data-processing modules into the broader data infrastructure.
Automation: Implement tools to automate data ingestion, cleaning, and synchronization across Python and C++ layers.
Multi-Agent Integration: Work with AI engineers to connect data pipelines into multi-agent research frameworks (feature generation, validation, and model retraining).
Performance Optimization: Benchmark system performance, identify bottlenecks, and improve throughput and reliability.
Monitoring & Validation: Build health checks and diagnostic dashboards for data quality and latency tracking.
Strong knowledge of Linux systems, including shell scripting, environment setup, and process management.
Proficiency in C++ (data structures, file I/O, multithreading preferred).
Experience with Python (pandas, multiprocessing, or FastAPI) for ETL and data orchestration.
Understanding of databases (ClickHouse, PostgreSQL, or similar).
Basic familiarity with AI/ML workflows (LangChain, LLM agents, or data preparation for model training).
Strong understanding of data engineering principles: pipeline design, error handling, schema evolution, and versioning.
Interest in financial data and quantitative systems.
Hands‑on exposure to real‑world AI‑driven quant infrastructure.
Experience working on high‑frequency, multi‑language systems (C++/Python/Linux).
Direct mentorship from quant researchers and AI engineers building next-generation agentic systems.
Opportunity to contribute to live research and production‑grade infrastructure.
Pathway to a full‑time Data or Systems Engineer position upon strong performance.
* Le salaire de référence se base sur les salaires cibles des leaders du marché dans leurs secteurs correspondants. Il vise à servir de guide pour aider les membres Premium à évaluer les postes vacants et contribuer aux négociations salariales. Le salaire de référence n’est pas fourni directement par l’entreprise et peut pourrait être beaucoup plus élevé ou plus bas.