Trading Analytics Developer, Quantitative Trading

Job description

The Quant Trading team is responsible for trading and managing risks associated with different crypto products, including spots and derivatives. The team develops and implements trading strategies in fast-paced and complex trading environments. We are seeking an experienced Trading Analytics Developer to join our Quant Trading team and play a pivotal role in advancing our 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. ### Job 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 ### Job 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