
Nuvama Wealth
4 days ago
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 Overvie :
- 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
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