Senior Data Engineer

Innowave Tech turns human expertise into intelligent systems that solve real industrial challenges. Headquartered in Singapore, we build AI platforms that help manufacturers inspect quality, understand complex processes and automate equipment operations.

Our work connects artificial intelligence, software and practical engineering with the realities of the factory floor. From detecting subtle defects to helping equipment respond to changing conditions, we turn specialist knowledge into capabilities that people can use, trust and scale.

We’re looking for people who ask thoughtful questions, care about how things work and take pride in making them better. Join us to tackle demanding problems, bring ideas into real operating environments and help shape how industry works next.

Establish the data engineering practice and build scalable, secure data infrastructure for semiconductor manufacturing AI solutions.

Select and manage on-premises technologies for secure, efficient operations.

Build pipelines to collect, clean, and transform process data, sensor data, image data, and human annotations.

Deploy secure, maintainable, and scalable data infrastructure.

Define and enforce data governance, privacy, and access-control practices.

Collaborate on deployment with cross-functional teams.

Polytechnic diploma or bachelor's degree in Computer Science, Engineering, or a related field.

At least 3 years of data engineering experience.

Track record of building scalable data systems from the ground up in a startup environment.

Proficiency in Python and/or Java for data pipeline development.

Experience with extract, transform, load (ETL) frameworks, such as Apache Airflow or Dagster, and streaming systems, such as Kafka.

Experience designing and maintaining SQL and NoSQL databases.

Experience building and operating data lakes and a data catalog.

Familiarity with Docker containerization, Git version control, and continuous integration and continuous delivery (CI/CD).

Communication, cross-functional collaboration, problem-solving, debugging, and engineering tradeoff skills.

Experience with on-premises or hybrid infrastructure.

Experience with manufacturing data systems, especially statistical process control (SPC), supervisory control and data acquisition (SCADA), and industrial sensor protocols such as OPC UA, MQTT, or Modbus.

Familiarity with artificial intelligence and machine learning (AI/ML) pipelines and tools such as MLflow.

Knowledge of vector databases and large language model (LLM) data infrastructure.

Experience working in or with regulated industries, such as semiconductor, automotive, or aerospace.