Data Engineer
Summary
Fully onsite, ~2-month contract in Singapore placing a mid/senior Data Engineer with a global consulting firm serving a regional bank. The engineer designs data architecture, builds reusable data pipelines and models, and prepares data for deposit analytics, ML and pricing decision-support.
Experience Mid Level (6-10 years) | Senior (11-15 years)
Duration 2 Months
Global Consulting Firm
Required Skills Data Engineering
Project Overview
Our client, a global consulting firm is working with a regional bank and is seeking an experienced Data Engineer to establish and optimise the data architecture and pipelines required to support deposit analytics, machine learning and pricing decision-making.
The consultant will build efficient, reusable data pipelines and data models to enable reliable data processing and support the development of deposit flow and customer elasticity models, as well as AI-enabled decision-support tools.
This is a fully onsite role based in Singapore for a duration of 6-8 weeks.
Key Responsibilities
- Design and implement data architecture to support deposit analytics and machine learning use cases
- Build efficient, scalable and reusable data pipelines to ingest, transform and process large datasets
- Develop and optimise data models to support deposit flow analysis, customer elasticity modelling and pricing analytics
- Prepare and structure data for downstream machine learning and AI engineering applications
- Ensure data quality, consistency, reliability and accessibility for analytical use cases
- Collaborate with Data Scientists and banking stakeholders to understand data requirements and deliver fit-for-purpose data solutions
- Document data architecture, pipeline logic and data models to support maintainability and future development
Requirements
- Minimum 5 years experience in data engineering, data architecture or a closely related technical role
- Strong experience designing data architectures and building efficient, reusable data pipelines
- Experience developing data models and preparing large datasets for machine learning use cases
- Strong technical problem-solving skills and an understanding of data quality and pipeline optimisation
- Banking, financial services or pricing analytics experience is highly preferred
- Strong communication skills and the ability to work directly with client stakeholders
