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

1. 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

1. 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.

1. 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