Quant Developer

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