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.

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

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

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

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

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

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

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