Quantitative Developer at Fintonia Group

Fintonia Group is a Singapore a licensed asset manager, with a growing presence in Kuala Lumpur, combining a strong background in traditional finance with disruptive technology.

You will be joining a fun and fast-paced environment and will be working with a small team of seasoned industry professionals with experience working at top tier global investment banks and industry leaders.

We are seeking a Quantitative Developer to join our team in Kuala Lumpur. This hands-on role will contribute directly to the design and implementation of cloud-based trading infrastructure and data pipelines that support a diverse set of digital asset strategies. Yo will be working with a small, experienced team with backgrounds in global investment banks and hedge funds.

The ideal candidate is highly technical, fluent in Python, experienced with cloud systems and databases, and motivated to build scalable, reliable tools for real-time trading and analytics. This is a high-impact role that supports the organization's continued innovation in the digital asset space, offering strong career development in a dynamic and fast-paced environment.

The Role:

As a Quantitative Developer at Fintonia Group, you will build and maintain the infrastructure that powers the organization's systematic and discretionary trading strategies. This includes integrating with exchanges, managing real-time data pipelines, developing execution logic, and enabling effective risk monitoring and collateral management.

Your primary responsibilities will include:

Real-Time Trading Infrastructure

Build and maintain Python-based execution systems for delta-neutral and options-based strategies
Integrate with exchange APIs (REST, WebSocket) across spot, futures, perpetuals, and options markets
Leverage cloud infrastructure (AWS, GCP, Azure) to deploy scalable and resilient trading systems
Implement low-latency components for market data ingestion and order tracking

Cloud Data Engineering & Database Management

Design and maintain cloud-native databases (e.g., PostgreSQL on RDS, BigQuery, Snowflake, TimescaleDB) for market, trade, and strategy data
Develop efficient schemas for high-frequency data (order books, trades, funding rates, volatility metrics)
Build automated data pipelines using tools like Airflow, Prefect, or AWS Step Functions
Manage historical and real-time datasets for backtesting, signal generation, and performance reporting
Ensure data quality, consistency, retention policies, and access controls across the platform
Implement data versioning and metadata tracking to support research reproducibility

Monitoring, Risk Management & Dashboards

Create dashboards using Streamlit, Dash, or Grafana to monitor strategies, markets, and system health
Set up alerting systems for key risk indicators such as margin levels, volatility spikes, and basis dislocations
Monitor database performance and query costs (especially in BigQuery/Snowflake), and optimize data usage

Qualifications and Experience

To be a successful applicant, you are likely to meet the following criteria:

At least 3-5 years of experience in Python development, ideally in a trading or financial environment
Strong experience with exchange APIs and real-time market data ingestion
Practical experience with cloud-based databases (e.g., AWS RDS, Redshift, BigQuery, Snowflake)
Proficient in SQL, schema design, and data modeling for high-frequency or time-series data
Familiar with data lake and data warehouse concepts, partitioning strategies, and performance tuning
Experience deploying to cloud environments (AWS/GCP/Azure), including Docker/Kubernetes and CI/CD pipelines
Familiarity with infrastructure-as-code tools (e.g., Terraform, CloudFormation) is a plus
Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field