Data Engineer

DYMON ASIA CAPITAL (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A financial technology firm in Singapore is seeking an experienced quant-focused data engineer to develop and optimize scalable data pipelines. This role involves delivering reliable data through APIs and collaborating with teams to enable advanced analytics and AI use cases. Candidates should possess a Bachelor's or Master's degree in Computer Science or Engineering, along with strong skills in Python, SQL, and familiarity with cloud platforms. A detail-oriented mindset and communication skills are essential for success in this fast-paced environment.

Qualifications

• 6–10 years of hands-on experience in data engineering.
• Experience building resilient data infrastructure in high-performance or financial environments.
• Familiarity with financial data nuances—traditional vs alternative, structured vs unstructured.

Responsibilities

• Design, build, and optimize scalable data pipelines for structured and unstructured datasets.
• Deliver timely, and reliable data through user-focused APIs for decision making.
• Shape data for advanced analytics and modeling.

Skills

Python

SQL

Spark

Kafka

Hadoop

Cloud platforms (Azure preferred)

Databricks

Snowflake

Redshift

Education

Bachelor’s or Master’s in Computer Science, Engineering, or related field

Tools

CI/CD

Lakehouse architectures

Open table formats (Iceberg, Delta Lake, Parquet)

Job description

Overview

We’re looking for an experienced quant focused data engineer to help us build our data lake. If you’re a curious, creative problem solver with 6–10 years of hands-on experience, this is your chance to shape the pipelines that power everything from multi-asset investment strategies to AI-driven insights. If you thrive in high-performance environments and love transforming raw data into highly available data products, we would love to hear from you.

Responsibilities

• End-to-End Pipelines Development: Design, build, and optimize scalable data pipelines for structured and unstructured datasets.
• Core Data Delivery: Collaborate with PMs to deliver timely, and reliable data through user-focused API’s for decision making.
• Quant-Ready Infrastructure: Partner with systematic teams to shape data for advanced analytics and modelling.
• AI-Ready Data: Work closely with the AI research, core engineering and UI teams to deliver data suitable for multiple AI use cases.
• Quality at Scale: Implement monitoring, validation, and remediation frameworks to ensure data accuracy and consistency.
• Governance Advocacy: Champion standards, lineage, metadata, and security across the data ecosystem.
• Automation & Efficiency: Drive ETL/ELT automation using cloud-native and distributed systems.
• Vendor Integration: Work with internal and external data providers to onboard and manage critical data assets.

Qualifications

• A Bachelor’s or Master’s in Computer Science, Engineering, or a related field.
• Strong coding skills in Python and SQL, with experience in distributed systems like Spark, Kafka, or Hadoop.
• Deep knowledge of lakehouse architectures and open table formats (Iceberg, Delta Lake, Parquet).
• Hands-on experience with cloud platforms (Azure preferred) and modern data warehouses (Databricks, Snowflake, Redshift).
• A proven track record of building resilient data infrastructure in high-performance or financial environments following CI/CD.
• Familiarity with financial data nuances—traditional vs alternative, structured vs unstructured, batch vs real-time— with strong awareness of point-in-time modelling requirements.
• A detail-oriented mindset, ownership mentality, and strong communication skills in fast-paced, collaborative settings.

Notes

EEO statements and other legal requirements are assumed to be handled separately and are not included here.