Data & Integration Engineer (GenAI & Enterprise Data Integration) - Banking

Unison Group; Singapore; On-site; SGD 90,000 - 160,000; Full time

Job summary

Unison Group is seeking an experienced Data & Integration Engineer to join the enterprise data team supporting GenAI initiatives. You will bridge system analysis, integration, and data engineering to ensure reliable data pipelines across platforms.

The role involves translating business requirements into scalable integration solutions, collaborating with application, security, and data platform teams, and delivering high-quality data for AI-powered business solutions.

Qualifications

• 5–10 years of experience in data engineering, system integration, and enterprise delivery.
• Ability to translate business requirements into scalable integration designs.
• Experience with upstream and downstream enterprise systems and data pipelines.
• Familiarity with GenAI data preparation and enterprise data platforms.

Responsibilities

• Analyze requirements and design end-to-end data flows and interface specifications.
• Develop and maintain enterprise data integrations using REST, SFTP, batch processing, and pipelines.
• Prepare data for GenAI initiatives including ingestion, aggregation, and enrichment.
• Collaborate with security, infrastructure, and data platform teams to ensure data quality and reliability.
• Document mappings, APIs, and operational procedures; support SIT/UAT and production support.

Skills

Data engineering

System integration

Enterprise app integration

Technical delivery

SQL

Python

Java

CI/CD

DevOps

Monitoring & observability

Education

Bachelor's degree in Computer Science, Information Technology, Engineering

Tools

Informatica

Cloudera

Git

Jira

Confluence

Control-M

Splunk

Elastic Stack

OpenTelemetry

Job description

Job Summary

We are seeking an experienced Data & Integration Engineer to join our enterprise data team supporting next-generation Generative AI (GenAI) initiatives. This role sits at the intersection of system analysis, enterprise integration, and data engineering, ensuring seamless movement, transformation, and availability of enterprise data across multiple platforms.

The ideal candidate will understand business requirements, translate them into scalable integration solutions, and work closely with application, infrastructure, security, and data platform teams to deliver reliable, high-quality data pipelines that enable AI-powered business solutions. This is an excellent opportunity for professionals who enjoy solving complex enterprise integration challenges and working across diverse technology ecosystems.

Key Responsibilities

1. System Analysis & Solution Design

• Analyze business and technical requirements and convert them into end-to-end system flows, data flows, and integration designs.
• Collaborate with business stakeholders and technical teams to define interface specifications and data contracts.
• Evaluate existing integration processes, identify gaps, inefficiencies, and risks.
• Recommend scalable, maintainable, and cost-effective integration solutions.
• Create technical documentation including architecture diagrams, interface mappings, and process documentation.

2. Enterprise Integration & Data Engineering

• Design, develop, and maintain enterprise data integrations using:
  • REST APIs
  • SFTP/File-based integrations
  • Batch processing
  • Enterprise data pipelines
• Develop scripts and automation programs to retrieve and process data from multiple enterprise systems.
• Build APIs to integrate applications and enterprise platforms.
• Coordinate data movement across enterprise Data Lake environments including Informatica, Cloudera, and related platforms.
• Ensure accurate data transformation, mapping, reconciliation, validation, and delivery.
• Troubleshoot integration failures and resolve production issues across multiple environments.

3. Data Preparation for Generative AI

• Support enterprise GenAI initiatives by preparing high-quality datasets for AI applications.
• Design and implement:
  • Document ingestion pipelines
  • Data aggregation processes
  • Data enrichment and transformation workflows
• Work with both structured and unstructured data sources.
• Prepare data for downstream AI use cases including:
  • Retrieval-Augmented Generation (RAG)
  • Semantic Search
  • Investigation workflows
  • Enterprise knowledge retrieval
• Ensure data quality, consistency, and readiness for AI consumption.

4. Delivery & Cross-functional Collaboration

• Work closely with:
  • Data Engineering teams
  • Application Development teams
  • Infrastructure teams
  • Security teams
  • Business stakeholders
• Participate in SIT, UAT, deployment, and production support activities.
• Implement monitoring, logging, scheduling, and error handling mechanisms.
• Support controlled releases following enterprise DevOps practices.
• Document integration flows, mappings, APIs, and operational procedures.

Required Skills & Qualifications

• Bachelor's Degree in Computer Science, Information Technology, Engineering, or a related discipline.
• 5–10 years of experience in:
  • Data Engineering
  • System Integration
  • Enterprise Application Integration
  • Technical Delivery
• Strong understanding of enterprise system architecture and end-to-end integration design.
• Proven experience translating business requirements into technical implementation plans.
• Experience working with upstream and downstream enterprise systems.

Technical Skills

• Strong SQL skills for querying, validation, reconciliation, and troubleshooting.
• Basic to intermediate Python programming for scripting, automation, and data processing.
• Exposure to Java development.
• Hands-on experience with:
  • REST APIs
  • SFTP
  • File-based integrations
  • Batch processing
  • Enterprise data pipelines
• Experience with:
  • Informatica (Preferred)
  • Cloudera or similar enterprise data platforms
• Working knowledge of:
  • Git
  • Branching strategies
  • Pull Requests
  • Code Reviews
• Familiarity with:
  • CI/CD pipelines
  • Jira
  • Confluence
  • Enterprise release processes
• Experience with Control-M or equivalent scheduling tools.
• Familiarity with monitoring and observability tools including:
  • Splunk
  • Elastic Stack
  • OpenTelemetry (OTEL)

GenAI Knowledge

Exposure to enterprise AI concepts including:

• Document ingestion
• Retrieval-Augmented Generation (RAG)
• Embeddings
• AI data preparation
• Enterprise search
• Knowledge retrieval workflows

Preferred Skills

• Experience with Informatica Data Integration.
• Exposure to enterprise Data Lake architectures.
• Experience supporting cloud-based data platforms.
• Understanding of enterprise security and governance standards.
• Knowledge of AI/ML data pipelines and modern data architectures.

Soft Skills

• Excellent analytical and problem-solving abilities.
• Strong communication and stakeholder management skills.
• Ability to challenge existing designs and recommend improved solutions.
• Strong system thinking with an end-to-end enterprise perspective.
• Ability to work effectively across cross-functional teams.
• Hands-on approach to troubleshooting while maintaining architectural oversight.
• Comfortable working in complex enterprise environments with multiple stakeholders.