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
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.
Kafka, Flink, and event processing in production environments


AI Platform Capabilities
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

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