Senior Quant Software Developer
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Key skills for this role
About the Role
Develop and maintain scalable solutions using Python, PySpark, and SQL. Design and implement data pipelines (ingestion, transformation, modelling, data quality checks, automatio.
Key Skills for This Role
Responsibilities
- Develop and maintain scalable solutions using Python, PySpark, and SQL.
- Design and implement data pipelines (ingestion, transformation, modelling, data quality checks, automation).
- Build and integrate APIs (REST, SOAP, gRPC), including authentication and authorization.
- Contribute to application architecture design, applying best practices and design patterns.
- Implement Test Driven Development (TDD) and ensure high code quality.
- Support and enhance CI/CD pipelines and DevOps processes.
- Work with cloud platforms (AWS, Azure) and data platforms such as Databricks and Palantir Foundry.
- Develop UI components and dashboards using Dash, JavaScript, and React.
- Integrate with market data and investment platforms.
- Support quantitative models and analytics (Monte Carlo simulations, pricing, risk, factor modelling).
- Contribute to portfolio management processes (asset allocation, rebalancing, attribution).
- Analyze complex datasets and deliver actionable insights for business and investment decisions.
Requirements
- Python (Advanced)
- PySpark (Medium)
- SQL (Advanced)
- Test driven Development (Advanced)
- CICD (Medium)
- DevOps (Advanced)
- Application Architecture design and development
- Data Engineering (Ingestion, Curation, Modelling, Automation, Data Quality Checks) (Preferred)
- APIs (patterns, authorization, SOAP, REST, GRPC) (Advanced)
- Knowledge/experience in financial markets and alternative asset classes (Hedge Funds)
- Knowledge/experience in end to end portfolio management
- Knowledge/experience with Quant processes (Monte Carlo simulation, pricing engines, optimization techniques, factor modelling, risk)
Full Job Posting
Responsibilities
- Develop and maintain scalable solutions using Python, PySpark, and SQL.
- Design and implement data pipelines (ingestion, transformation, modelling, data quality checks, automation).
- Build and integrate APIs (REST, SOAP, gRPC), including authentication and authorization.
- Contribute to application architecture design, applying best practices and design patterns.
- Implement Test Driven Development (TDD) and ensure high code quality.
- Support and enhance CI/CD pipelines and DevOps processes.
- Work with cloud platforms (AWS, Azure) and data platforms such as Databricks and Palantir Foundry.
- Develop UI components and dashboards using Dash, JavaScript, and React.
- Integrate with market data and investment platforms.
- Support quantitative models and analytics (Monte Carlo simulations, pricing, risk, factor modelling).
- Contribute to portfolio management processes (asset allocation, rebalancing, attribution).
- Analyze complex datasets and deliver actionable insights for business and investment decisions.
Must Have Skills
- Python (Advanced)
- PySpark (Medium)
- SQL (Advanced)
- Dash (Python framework), JavaScript (Medium), React (Preferred), OpenFin / FTC3 (Preferred)
- Azure (Preferred), AWS (Medium)
- DataBricks (Preferred), Palantir Foundry (Medium)
- Test driven Development (Advanced), CICD (Medium), DevOps (Advanced)
- Application Architecture design and development (Design patterns etc)
- Data Engineering (Ingestion, Curation, Modelling, Automation, Data Quality Checks) (Preferred)
- APIs (patterns, authorization, SOAP, REST, GRPC) (Advanced)
- Data Science (Preferred)
- Understand and provide insights on emerging industry trends.
Domain Knowledge
- Knowledge / experience in financial markets and alternative asset classes (Hedge Funds)
- Knowledge / experience in end to end portfolio management (asset allocation, portfolio construction, rebalancing, implementation, attribution)
- Experience with sourcing data from, and integrating with, core investment and/or market data platforms
- Knowledge / experience with Quant processes (Monte Carlo simulation, pricing engines, optimization techniques, factor modelling, risk)
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