Back-End Engineer - Analyst, Risk & Quant Analytics

Job Brief

Our team specializes in data engineering, quantitative research, portfolio analytics, and risk management systems that support investment and trading decisions. We develop end-to-end solutions covering market data pipelines, portfolio and factor calculation engines, database design, APIs, analytical tools, and user-facing applications.

Many of our projects involve processing large-scale financial datasets, maintaining research-quality data platforms, and building systems that enable quantitative researchers and portfolio managers to access timely, accurate, and reliable data. Team members are expected to develop solutions that are robust, scalable, and maintainable while ensuring analytical accuracy and data integrity throughout the entire data lifecycle.

We use Agile and DevOps methodologies to refine our products iteratively, managing build, test, deployment, monitoring, and operational support processes. We maintain high-quality code through peer reviews and automated testing, leveraging Git workflows, CI/CD pipelines, cloud-native technologies, and operational excellence practices.

Requirements

• A minimum of 3 years of experience in either front-end or back-end development, or both. While you are not expected to be an expert in all areas, you should be comfortable working across different layers of the technology stack and be willing to develop end-to-end solutions.
• Eagerness to step out of your comfort zone to learn and to make a difference. You will encounter software engineering problems, quantitative finance concepts and real-world production challenges that require translating your talent and passions into production-grade systems.
• Strong backend software engineering skills with experience developing data-intensive applications and processing large datasets.
• Experience designing, developing, and operating data pipelines, ETL/ELT processes, APIs, analytical data platforms and workflow orchestration, scheduling tools such as Apache Airflow.
• Familiarity with monitoring, alerting, troubleshooting, and operational support for business-critical data platforms.
• Experience operating production-grade data platforms with a focus on reliability, scalability, and operational resilience.
• Experience implementing data quality controls, data validation frameworks, and operational safeguards to ensure consistency across research and production environments.
• Proficiency in English reading and writing. Our team members come from diverse backgrounds, and daily communication is primarily in English, while limited speaking ability is acceptable.

Preferred Qualifications

• Familiarity with our primary languages and frameworks, including C#, TypeScript, Python, React, FastAPI, and .NET Core.
• Experience with SQL/NoSQL databases, OLAP databases such as StarRocks, Apache Airflow, New Relic, Anaconda, RESTful APIs, WebSocket and gRPC.
• Knowledge of cloud platforms, CI/CD pipelines, Infrastructure-as-Code (CDK/Terraform), Kubernetes, Docker, and modern observability platforms.
• Experience designing performant data processing solutions for large-scale portfolio analytics and factor calculations. Familiarity with concurrent processing, task parallelization, multi-threading, distributed processing frameworks, or other techniques used to reduce calculation time for portfolio exposures, factor loadings, risk metrics, and large-scale analytical workloads is a plus.
• Strong mathematical and statistical foundations, including probability, correlation, covariance, regression analysis, linear algebra, matrix operations, and optimization techniques commonly used in quantitative finance.
• Experience building and supporting quantitative data platforms, including:
• Market data ingestion and processing
• Factor calculation engines
• Portfolio analytics systems
• Research databases and data marts
• Data distribution services and APIs
• High-volume analytical workloads requiring efficient data processing and computational optimization
• Understanding portfolio factor models, factor exposures, factor loadings, risk models, security master data, corporate actions, benchmark data, and related investment management concepts is highly desirable.

A financial background is not a prerequisite. We recognize that candidates may not have prior experience in hedge funds or investment banks. Our domain knowledge is accessible and can be acquired through hands-on involvement in quantitative research, portfolio analytics, and risk management projects.