Trading Analytics Developer, Quantitative Trading

About the role

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

- Design and build unified analytics engine designed for processing large-scale data using Apache Spark and related tools

- 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

- 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, web-based interactive dashboards and BI tools etc.