GenAI Data Engineer - Enterprise Pipelines & AI Data

re-zoo-me; Singapore; Hybrid; SGD 120,000 - 160,000; Full time

Job summary

unknown is seeking an experienced Data Engineer to own end-to-end data solutions in a hybrid-cloud environment. The role focuses on building scalable data pipelines using Databricks, Apache Spark and Kafka, and laying foundations for analytics, ML and AI capabilities.

The ideal candidate has 5–8 years in data engineering with strong Python and SQL skills, experience with GenAI data solutions and familiarity with Databricks, Delta Lake and Microsoft Fabric.

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Information Technology or related technical discipline.
• 5–8 years of professional experience spanning data engineering, data platforms, cloud data solutions or large-scale analytics engineering.
• Experience delivering robust data pipelines at scale with orchestration, fault handling and production operations.
• Strong programming and data manipulation capabilities in Python and SQL.
• Practical experience with Apache Spark / PySpark and distributed data processing at scale.
• Experience developing knowledge management, Retrieval-Augmented Generation (RAG) or retrieval-based data solutions for GenAI.
• Exposure to Databricks, Kafka, Delta Lake and Microsoft Fabric.
• Solid understanding of data platform architecture, cloud environments, security controls and CI/CD.

Responsibilities

• Own the end-to-end engineering of enterprise data solutions, from ingestion to data consumption in a hybrid-cloud environment.
• Engineer high-volume batch and real-time data pipelines using Databricks, Spark and Kafka.
• Establish data foundations for analytics, ML and AI, ensuring data is accessible and trusted.
• Drive engineering and productionisation of GenAI data capabilities including RAG architectures.
• Build data processing and transformation logic using Python, PySpark and SQL.

Job description

unknown is seeking an experienced Data Engineer to own end-to-end data solutions in a hybrid-cloud environment. The role focuses on building scalable data pipelines using Databricks, Apache Spark and Kafka, and laying foundations for analytics, ML and AI capabilities.

The ideal candidate has 5–8 years in data engineering with strong Python and SQL skills, experience with GenAI data solutions and familiarity with Databricks, Delta Lake and Microsoft Fabric.