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Quantitative Data Engineer - Trading Systems

ConfidentialMumbai, India

5 days ago

Job description

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

Skills Required

Java, Rust, Scala, Pyspark, Kafka, Kinesis, Influxdb, Databricks, Python, Aws

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