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