Lead Data & Integration Engineer

About the role

Lead Data & Integration Engineer

Important Information

Location: Singapore

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

- 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

- 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.

- 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

Key Requirements

Below are the key skillsets that will be required for all relevant tasks mentioned:

- 10 years of experience in system analysis, integration engineering, data engineering or technical delivery roles.

- Strong ability to translate requirements into system flows, data flows, interface specifications and implementation plans.

- Experience working with upstream and downstream teams to define and deliver enterprise integrations.

- Practical experience with REST APIs, SFTP, batch processing, file based integration and data pipeline orchestration.

- Good understanding of data mapping, transformation, aggregation, reconciliation and data quality controls.

- Good SQL skills and basic to moderate Python skills for data handling, scripting, automation and troubleshooting.

- Exposure to Java

- Exposure to Informatica, Cloudera or similar enterprise data platforms.

- Working knowledge of Git, branching, pull requests, code reviews and controlled release practices.