Quantitative Data Engineer

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

• 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

• 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

• 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

• 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