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, Data Engineer, 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
- Databricks expertise: Cluster management, Delta Lake, streaming architectures
- 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
- Multi-Asset Expertise: Equities, derivatives (futures/options), commodities, forex trading mechanics
- Market Data Vendors: Bloomberg API, Reuters, NSE/BSE direct feeds, vendor data normalization
- Indian Markets: Understanding of NSE/BSE operations, SEBI regulations, and local market practices
5. Behavioral Competencies
Technical Leadership & Innovation
- Solution Design: Architects elegant solutions for complex technical and business requirements
- Creative Problem-Solving: Develops innovative approaches to performance bottlenecks and system constraints
- Technology Adoption: Evaluates and integrates emerging technologies to maintain competitive advantage
- Quality Focus: Implements robust testing, monitoring, and alerting for mission-critical trading systems
Collaboration & Stakeholder Management
- Trader Partnership: Translates complex technical concepts into business impact for trading stakeholders
- Requirements Gathering: Actively listens to trading desk needs and converts them into technical specifications
- Cross-functional Communication: Effectively coordinates with risk, compliance, and operations teams
- Documentation: Creates comprehensive technical documentation for system maintenance and knowledge transfer
Execution & Delivery
- Project Leadership: Takes ownership of end-to-end system delivery with minimal supervision
- Agile Methodology: Thrives in fast-paced, iterative development cycles with changing requirements
- Performance Mindset: Obsessed with system performance, latency optimization, and operational excellence
- Risk Awareness: Understands the financial impact of system failures and implements appropriate safeguards
Financial Markets Acumen
- Trading Intuition: Understands how technical decisions impact trading strategies and profitability
- Market Dynamics: Grasps the relationship between market events and system performance requirements
- Regulatory Mindset: Considers compliance and audit requirements in system design decisions
- Commercial Awareness: Balances technical perfection with business deadlines and budget constraints
Continuous Learning & Adaptation
- Technology Curiosity: Stays current with developments in quantitative finance, data engineering, and trading technology
- Market Evolution: Adapts systems and approaches as market structure and regulations evolve
- Performance Improvement: Continuously benchmarks and optimizes system performance metrics
- Knowledge Sharing: Contributes to team learning through code reviews, technical discussions, and documentation
Technology Stack Overview
Core Languages & Frameworks
- High-Performance: C++, Rust for ultra-low latency components
- Data Processing: PySpark, Scala for large-scale data transformation
- Application Development: Java, Python for business logic and APIs
- Analytics: Python (pandas, numpy, scipy) for quantitative analysis
Infrastructure & Platforms
- Cloud: AWS (EC2, S3, RDS, Kinesis, Lambda)
- Big Data: Databricks, Apache Spark, Delta Lake
- Databases: InfluxDB, TimescaleDB, ClickHouse, PostgreSQL
- Monitoring: CloudWatch, Grafana, custom alerting systems
Trading & Market Data
- Backtesting: Zipline, Backtrader, QuantConnect, bt, PyAlgoTrade
- Market Data: Bloomberg API, Reuters, NSE/BSE feeds
- Order Management: Custom OMS development, FIX protocol integration
- Risk Systems: Real-time position tracking, limit monitoring
