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