Trading Analytics Developer, Quantitative Trading

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…