Data Engineer: Pipelines for Factory Digital Twins

GMP TECHNOLOGIES (S) PTE LTD; Singapore; On-site; SGD 90,000 - 120,000; Full time

Job summary

GMP Technologies (S) PTE LTD is seeking a data engineer to build and deploy data integration pipelines on the factory floor, connecting MES, ERP, and IoT systems to the Omniverse platform. The role focuses on ETL development, data lake/warehouse architecture, and real-time digital twin data support.

You will collaborate with Digital Twin Engineers to set data interface standards, monitor pipeline health, and resolve anomalies, ensuring data accuracy and timeliness across complex manufacturing

Qualifications

• 3+ years hands-on data engineering or ETL development.
• Proficiency in Python and SQL, plus experience with stream processing tools such as Apache Kafka.
• Practical experience with industrial communication protocols, including REST API, MQTT, and OPC-UA.
• Solid knowledge of database systems such as PostgreSQL, MongoDB, and InfluxDB.

Responsibilities

• Build and deploy data integration pipelines for the factory floor, linking MES, ERP, and IoT systems to the Omniverse platform.
• Develop ETL workflows that keep data accurate, timely, and consistent.
• Architect data lakes and data warehouses that power real-time, data-driven digital twins.
• Work with Digital Twin Engineers to define data interface standards and transmission protocols.
• Oversee the health of data pipelines, respond to alerts, and resolve faults and anomalies.

Skills

Python

SQL

Kafka

REST API

MQTT

OPC-UA

PostgreSQL

MongoDB

InfluxDB

Tools

ETL

Job description

GMP Technologies (S) PTE LTD is seeking a data engineer to build and deploy data integration pipelines on the factory floor, connecting MES, ERP, and IoT systems to the Omniverse platform. The role focuses on ETL development, data lake/warehouse architecture, and real-time digital twin data support.

You will collaborate with Digital Twin Engineers to set data interface standards, monitor pipeline health, and resolve anomalies, ensuring data accuracy and timeliness across complex manufacturing