Singapore Finance Data Engineer

Firstplus; Singapore; On-site; SGD 70,000 - 100,000; Full time

Benefits offered by this job

Hands-on exposure to AI applications in finance

Practical experience in financial data management

Opportunity to contribute to impactful projects

Job summary

A financial technology company in Singapore is seeking a Finance Data Engineer to optimize data pipelines and build AI-driven solutions. This role requires strong Python skills and experience with REST APIs, cloud workflows, and financial data analytics. You will work closely with portfolio managers and analysts to translate business needs into data-driven solutions. The position offers hands-on exposure to AI applications in finance and opportunities to contribute to impactful projects that shape investment decisions.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science and Business Analytics or a related field.
• Relevant experience in financial data analytics or technology roles.
• Strong foundation in Python for data analysis and AI/ML model development.
• Experienced with APIs: REST, JSON, authentication methods (OAuth, tokens).
• Understanding of financial instruments and asset management concepts.

Responsibilities

• Build and maintain core data pipelines using AWS databases.
• Maintain REST APIs for market and fund data.
• Handle market data extractions and transformations.
• Collaborate with portfolio managers and analysts for data-driven solutions.

Skills

Python

REST APIs

Data analysis

AI/ML

AWS

Education

Bachelor’s or Master’s degree in Computer Science, Data Science or related field

Tools

AWS databases

VPN connectivity

Job description

We are seeking a Finance Data Engineer with strong analytical and technical skills to join our team. This role offers the opportunity to work at the intersection of finance and technology by building AI-driven solutions and managing financial datasets to support strategic investment decisions.

As part of our Data Intelligence Team, you will play a key role in designing, implementing, and optimizing data pipelines, AI models, and analytical tools that drive business insights and improve investment performance.

Key Responsibilities

• Build & Maintain Core Data Pipelines
  • Work with AWS databases to manage, clean, and query large financial datasets. Optimizing database queries to ensure efficiency in extracting and processing data.
• REST API & SYSTEM INTEGRATION
  • Maintain internal and external REST APIs used for market data, fund data, and operational systems via POSTMAN or EC2.
• Handle market data extractions, transformations, fund performance requests, and reusable scripts for recurring queries.
• Act as a central point of accountability for data pipelines and data integrity.
• Work closely with portfolio managers, analysts, and technology teams to translate business needs into data-driven solutions.
• Contribute to ongoing projects and propose innovative ideas that leverage AI and automation in finance.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science and Business Analytics or a related field.
• Relevant experience in financial data analytics or technology roles.
• Strong foundation in Python for data analysis and AI/ML model development.
• Experienced with APIs: REST, JSON, authentication methods (OAuth, tokens).
• Experienced with VPN connectivity, cloud workflows, and secure data access.
• Completed Projects related to machine learning and deep learning concepts is a plus.
• Understanding of financial instruments and asset management concepts.

What You’ll Gain

• Hands-on exposure to AI applications in finance.
• Practical experience in financial data management and asset performance analysis.
• Opportunity to contribute to impactful projects that shape investment decisions.
• Direct impact on automation, operational efficiency, and data quality across the organization.

Interviewing Process

• Pre-Selection: Take-home technical assessment focused on Data Structures & Algorithms (DSA)
• Round 1 – Technical Interview: Discussion of the take-home assessment and evaluation of core technical knowledge (DSA, APIs, VPNs, Cloud Computing)
• Round 2 – Fit Interview: Assessment of role alignment, problem-solving approach, and cultural fit
• Round 3 – Management Interview: Final discussion with leadership focusing on experience, expectations, and growth potential