DE&A - Core - Big Data Engineering - Hadoop Ecosystem
Key Responsibilities
1. Quantitative Model Design & Development
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Architect and develop financial models for pricing, risk, and trading strategies
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Design frameworks for derivatives pricing, portfolio optimization, and risk analytics
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Ensure models are scalable, reusable, and production-ready
2. Architecture & Platform Development
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Define and drive end-to-end architecture for quant platforms
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Build high-performance systems using Python/C++/Java
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Integrate models with data pipelines, APIs, and cloud platforms (AWS/Azure/GCP)
3. Risk & Analytics Solutions
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Develop solutions for market risk, credit risk, liquidity risk, and PnL attribution
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Architect real-time and batch analytics systems for large datasets
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Ensure compliance with regulatory frameworks (Basel III, IFRS9, etc.)
4. Collaboration & Stakeholder Management
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Work closely with quants, traders, risk teams, and data engineers
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Translate business requirements into technical and analytical solutions
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Provide technical leadership and mentoring to quant developers
5. Performance Optimization & Governance
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Optimize model performance and computational efficiency
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Implement validation frameworks, backtesting, and model governance
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Ensure code quality, maintainability, and version control
Mandatory Skills
Quantitative & Financial Expertise
Strong knowledge of:
Derivatives pricing (Black-Scholes, Monte Carlo, etc.)
Fixed income, equities, FX, and structured products
Risk frameworks (VaR, CVA, stress testing)
Technical Skills
Programming: Python (Must), C++/Java (Preferred)
Libraries: NumPy, Pandas, SciPy, TensorFlow (good to have)
Experience with big data tools (Spark, Hadoop)
Database knowledge: SQL, NoSQL
Data & Architecture
Experience designing data-driven architectures and APIs
Hands-on with cloud platforms (AWS/Azure/GCP)
Familiar with microservices architecture
Good-to-Have Skills
Exposure to AI/ML in quantitative finance
Knowledge of Databricks, Snowflake, or similar platforms
Experience with real-time streaming (Kafka, Flink)
Certification like FRM, CFA, CQF
Experience & Qualifications
Education: Bachelor’s/Master’s/PhD in Mathematics, Finance, Engineering, or related field
Experience: 8–15 years in quantitative development/architecture
Strong experience in financial services (Banking, Capital Markets, Hedge Funds)
Key Competencies
Strong analytical and problem-solving skills
Ability to simplify complex financial concepts
Leadership and stakeholder management
High attention to detail and accuracy
Typical Use Cases Delivered
Pricing engines for derivatives
Risk analytics platforms (VaR dashboards, stress testing tools)
Algorithmic trading systems
Portfolio optimization frameworks