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Quantitative Data Engineer Job Opening In Singapore, Singapore – Now Hiring Qumata


Job description

Introducing Qumata

Qumata is an innovative data science firm built on a foundation of applying cutting-edge machine learning and proprietary technology to complex, real-world problems.

Our core mission is to empower decision-making by transforming raw data into predictive insights.

We began our journey by successfully applying our expertise to the InsureTech space, where our solutions continue to deliver significant value and drive ongoing sales.

Building on this success and demonstrating the versatility of our data science models, we have expanded our strategic focus into the financial markets.

We have achieved significant proprietary trading success, most recently in the high-growth short-dated U.S. index option market This expansion is a major new chapter for Qumata, representing an exciting horizon for business growth and a unique opportunity to apply sophisticated machine learning and AI/ML talent to high-value trading strategies.

We are now actively scaling this new financial trading unit and are looking for passionate, problem-solving Data Scientists to join us in building the next generation of algorithmic trading models.

About the role

As a Quantitative Infrastructure Engineer, you will design, build, and maintain the production backbone that enables live trading and backtesting of 0DTE options strategies.

You will be responsible for containerized deployments, order routing integration, and data pipeline orchestration.

The role requires deep technical fluency in Python, cloud systems, and automation — with a strong sense of reliability, performance, and operational discipline.

Because the systems support live U.S. market trading, you may occasionally be on standby during U.S. trading hours to monitor execution health, debug incidents, and ensure uptime across trading environments.

Key Responsibilities include, but are not limited to:

  • Build and maintain cloud infrastructure (AWS EC2, S3) for live and batch trading operations.

  • Develop and automate deployment pipelines (CI/CD) for strategy updates, container images, and job scheduling.

  • Implement event-driven order execution via broker APIs (e.g., Interactive Brokers TWS/IB Gateway).

  • Manage data ingestion and transformation pipelines for market, volatility, and execution data.

  • Optimize latency, resource usage, and system resilience under real-time trading conditions.

  • Set up monitoring and alerting (Prometheus, Grafana) to track performance, PnL metrics, and infrastructure health.

  • Maintain clean, modular Python codebases for backtesting, execution, and reconciliation tools.

  • Collaborate with strategy and trading teams to deploy and validate new versions of production logic.

  • Support production troubleshooting and post-mortem analysis when system anomalies occur.

Qualifications

  • Education: Degree in Computer Science, Software Engineering, Quantitative Finance, or related technical field.

  • Technical Skills: Strong proficiency in Python (asyncio, multiprocessing, REST/websocket APIs), Solid understanding of AWS cloud infrastructure — EC2, S3, IAM, AND Experience with Docker, GitHub Actions, and CI/CD deployment pipelines.

  • Familiarity with broker APIs (Interactive Brokers, Alpaca, Tradier, etc.) and real-time order execution logic.

  • Proficiency with SQL databases and columnar storage engines (PostgreSQL, ClickHouse).

  • Comfort with Linux server administration, cron scheduling, and process automation.

  • Experience with monitoring and logging frameworks (Prometheus, Grafana, ELK stack).

  • Bonus Experience: Knowledge of market microstructure and options trading workflows, Familiarity with Kubernetes, Docker, AND Prior experience supporting real-time trading or financial systems in production.

Required Skill Profession

Other General


  • Job Details

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Unlock Your Quantitative Data Potential: Insight & Career Growth Guide


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The Work Culture

An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at Qumata adheres to the cultural norms as outlined by Expertini.

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7. Obeying Singapore laws and regulations

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Interview Tips for Quantitative Data Engineer Job Success

Qumata interview tips for Quantitative Data Engineer

Here are some tips to help you prepare for and ace your Quantitative Data Engineer job interview:

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