Quant Developer - Trading Systems

Purpose

Design and build high-performance trading systems and data infrastructure from the ground up for Nuvama's capital markets operations. This role combines quantitative finance expertise with cutting-edge Data Engineering to create real-time trading execution systems, market data pipelines, and risk management platforms that directly impact trading profitability and operational efficiency.

1. Functional Responsibilities / KPIs

Primary Responsibilities

Trading System Development : Build live trading execution systems, order management platforms, and order book management systems from scratch

Real-time Data Infrastructure : Design and implement high-throughput market data ingestion and preprocessing pipelines using Databricks and AWS

Backtesting Frameworks : Develop comprehensive backtesting and simulation engines for strategy validation across multiple asset classes

Solution Architecture : Create scalable system designs that handle market volatility and high-frequency data streams

Trader Collaboration : Work directly with traders and portfolio managers to understand requirements and build custom solutions

Performance Optimization : Ensure ultra-low latency execution and real-time risk monitoring capabilities

Key Performance Indicators

System Performance : Achieve sub-millisecond latency for critical trading operations

Data Accuracy : Maintain 99.99% data integrity across all market data feeds

System Uptime : Deliver 99.9% availability during market hours with zero trading halts due to system issues

Processing Throughput : Handle 1M+ market data updates per second during peak trading

Project Delivery : Complete trading system modules within agreed timelines

Trader Satisfaction : Achieve 90%+ satisfaction scores from trading desk stakeholders.

2. Qualifications

Educational Requirements

Bachelor's / Master's degree in Computer Science, Engineering, Mathematics, Physics, or Quantitative Finance

Strong foundation in data structures, algorithms, and system design principles

Understanding of financial markets, trading mechanics, and quantitative methods

Technical Certifications (Preferred)

AWS certifications (Solutions Architect, Developer, or Developer)

Databricks certifications in data engineering or analytics

Financial industry certifications (CQF, FRM) are advantageous

3. Experience

Required Experience

2-5 years of hands-on experience in quantitative finance or financial technology

Recent experience (within last 2 years) working with equity markets and trading systems

Proven track record of building trading systems, backtesting frameworks, or market data infrastructure

Experience with high-frequency data processing and real-time streaming systems

Direct collaboration experience with trading desks or portfolio management teams

Preferred Experience

Previous experience at investment banks, hedge funds, prop trading firms, or fintech companies

Experience building systems from scratch rather than maintaining legacy applications

Background in algorithmic trading strategy development and implementation

Exposure to Indian capital markets (NSE / BSE) and regulatory requirements (SEBI compliance)

Leadership experience in technical projects or mentoring junior developers

4. Functional Competencies

Programming & Development

Expert-level proficiency in at least 2 of : PySpark, Scala, Rust, C++, Java

Python ecosystem : Advanced skills in pandas, numpy, scipy for quantitative analysis

Performance optimization : Experience with memory management, parallel processing, and low-latency programming

API development : RESTful and WebSocket APIs for real-time market data distribution

Data Engineering & Infrastructure

AWS services : EC2, S3, RDS, Kinesis, Lambda, CloudFormation for scalable deployments

Database technologies : Time-series databases (InfluxDB, TimescaleDB), columnar stores (ClickHouse), traditional RDBMS

Streaming technologies : Real-time data processing frameworks (Kafka, Kinesis, Apache Spark Streaming)

Trading Systems Architecture

Order Management Systems : Order routing, execution algorithms, and trade lifecycle management

Market Data Processing : Tick data ingestion, order book reconstruction, and market microstructure analysis

Risk Management : Real-time position monitoring, limit checking, and risk control systems

Backtesting Frameworks : Zipline, Backtrader, QuantConnect, bt, PyAlgoTrade, and custom framework development

Financial Markets Knowledge

Equity Markets : Order types, market microstructure, settlement cycles, and trading regulations