Trading Analytics Developer, Quantitative Trading
Our client is seeking an experienced Trading Analytics Developer to join the Quant Trading team and play a pivotal role in advancing their data and AI infrastructure. This role combines traditional quantitative development with cutting-edge AI platform engineering, focusing on building robust, scalable systems that serve both data analytics and artificial intelligence workloads. The ideal candidate will bridge the gap between high-performance trading systems and modern AI capabilities, ensuring reliability, performance, and actionable insights across both domains.
Core Responsibilities
- Data Platform & Analytics
- Design, build, and operate high throughput batch and streaming data pipelines using Kafka, Flink, and ETL technologies
- Develop and optimize analytical data models for time-series, financial metrics, and trading activity
- Implement and manage analytical databases (ClickHouse, MongoDB, BigQuery, Snowflake, or similar) with cost-aware architecture
- Build idempotent data pipelines with robust backfill and reconciliation capabilities
- Create comprehensive monitoring for data quality, freshness, and pipeline reliability
- AI Platform Development
- Design, build, and operate internal AI platforms serving multiple trading teams
- Develop vector search systems with optimized HNSW indexing and hybrid retrieval capabilities
- Implement evaluation frameworks for retrieval quality (Recall@K, MRR, nDCG) and RAG systems
- Build reusable AI tooling including standardized RAG pipelines, prompt management, and self-service workflows
- Create and maintain agent systems using modern frameworks (LangGraph, A2A, MCP) with focus on controllability and auditability
Technical Requirements
- Mandatory Foundations
- 5+ years production experience with both Python and Java in high-performance environments
- Strong software engineering fundamentals: system design, data structures, algorithms, data integrity, accuracy and performance optimization
- Expertise in Linux, Github, and modern CI/CD practices
- Proven experience with AWS cloud services and Kubernetes orchestration
- Comfort working with large-scale, complex datasets in financial/trading contexts
- Data Platform Expertise
- Advanced SQL with window functions and query optimization, realtime data synchronization together with database design and infrastructure support
- Experience with data workflow and messaging orchestration (Airflow, Jenkins, AMPS etc.)
- Metric design and implementation for trading analytics (PnL, risk, balance and trade reconciliation, backfill and performance tuning)
- Time-series data visualization with Grafana, TradingView and BI tools
- Kafka, Flink, and event processing in production environments
- AI Platform Capabilities
- Vector search system design and optimization (recall/latency/memory trade-offs)
- Retrieval system evaluation methodologies and quality frameworks
- RAG pipeline architecture and optimization techniques
- LLMOps practices including model lifecycle and prompt management
- Experience with AI agent frameworks in production settings like A2A and MCP
- LangGraph / LangChain to build AI workflow and to connect AI models with data and tools to create smarter applications
Preferred Qualifications
- Financial/Trading Domain
- Experience in trading systems, quantitative finance, or financial technology
- Understanding of market data, data subscription using Rest API / Web Socket
- Knowledge of cryptocurrency markets, defi, and related technologies
- Professional Attributes
- Excellent problem-solving skills with ability to perform under pressure
- Strong communication skills for cross-team collaboration
- Proactive approach to system reliability and performance optimization
- Continuous learning mindset in rapidly evolving AI/ML landscape
- Balance of practical engineering rigor with innovative solution development
