Data Engineer: AI Infrastructure & Pipelines

DADACONSULTANTS PTE. LTD.; Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

DADACONSULTANTS PTE. LTD. is seeking a Data Engineer to build and optimize large-scale data pipelines that support AI model development. You will collaborate with AI, Data, and Engineering teams to process datasets, improve data quality, and build reliable data infrastructure.

Responsibilities include creating scalable pipelines, developing data cleaning and ETL tools, and monitoring performance to ensure robust data delivery for AI workflows and model training.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related discipline.
• 2–6 years of experience in Data Engineering, Big Data, or Software Engineering.
• Strong programming skills in Python.
• Experience with SQL and relational databases.
• Experience with at least one distributed data processing framework such as Spark or Ray.
• Familiarity with data pipelines and ETL workflows.
• Good problem-solving skills and ability to work with large datasets.
• Strong communication and teamwork skills.

Responsibilities

• Build and maintain scalable data processing pipelines.
• Develop tools for data cleaning, transformation, filtering, and data preparation.
• Optimize data processing performance and improve pipeline efficiency.
• Work with large datasets to support AI model training and evaluation.
• Collaborate with AI and engineering teams to ensure reliable and high-quality data.
• Monitor pipeline performance and troubleshoot production issues.
• Improve data quality, reliability, and automation across the platform.

Skills

Python

SQL

Spark/Ray

Data pipelines

ETL

Communication

Teamwork

Education

Bachelor's degree

Tools

Kafka

Flink

Iceberg

Delta Lake

AWS

Kubernetes

Docker

Job description

DADACONSULTANTS PTE. LTD. is seeking a Data Engineer to build and optimize large-scale data pipelines that support AI model development. You will collaborate with AI, Data, and Engineering teams to process datasets, improve data quality, and build reliable data infrastructure.

Responsibilities include creating scalable pipelines, developing data cleaning and ETL tools, and monitoring performance to ensure robust data delivery for AI workflows and model training.