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