Data & Integration Engineer - GenAI

About the Client:

Our client is an established banking and financial services organization in Singapore, the role will be within their technology department

Responsibilities:

We are looking for a Data & Integration Engineer who operates effectively at the intersection of system analysis, enterprise integration and data engineering to support GenAI initiatives.

The successful candidate will:

. Understand business and functional requirements

. Translate them into data flows and integration designs

. Work across upstream and downstream systems

. Ensure reliable movement, transformation and availability of data for GenAI use cases

. Develop scripts / programs to get data from different integration systems

. Develop APIs to integrated with relevant systems.

The focus is on getting data into the right place, in the right format, reliably.

What Matters Most

. Strong system thinking and ability to understand end to end flows

. Ability to challenge bad designs and propose better ones

. Strong stakeholder communication

. Hands on enough to troubleshoot and implement, but not a pure coder

. Experience navigating messy enterprise ecosystems

Key Responsibilities
1. System Analysis & Design

• Analyse business/technical requirements and translate them into data flows and integration designs
• Work with upstream and downstream teams to define data contracts and interfaces
• Identify gaps, inefficiencies and risks in current data movement processes
• Propose pragmatic solutions balancing speed, quality and maintainability

2. Integration & Data Movement

• Design and implement data movement across systems using:
• APIs
• SFTP and file based transfers
• Batch pipelines
• Coordinate integrations across systems in the DataLake ecosystem (Informatica, Cloudera, etc.)
• Ensure data is correctly transformed, mapped and delivered to target systems
• Troubleshoot integration issues across environments

3. Data Preparation for GenAI

• Support data ingestion and preparation for GenAI use cases:
• document ingestion
• data aggregation
• enrichment and transformation
• Work with structured and unstructured data
• Ensure data is usable for downstream AI workflows (RAG, search, investigation flows)

You are not asking them to build models, just make data usable for them.

4. Delivery & Coordination

• Work across multiple teams:
• data platforms
• application teams
• infrastructure
• security
• Support SIT, UAT and production rollouts
• Ensure integration reliability, error handling and monitoring
• Document flows, mappings and interfaces clearly