Data Engineer

PSA Corporation; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

PSA Corporation in Singapore is seeking a Data Engineer to design, develop, and maintain scalable data solutions for analytics and ML projects. You will build ETL/ELT pipelines, APIs, dashboards, and collaborate with data scientists and software engineers to deploy data products.

The role requires a degree in CS/CE and 2–3 years of experience in data or software engineering, with strong SQL/NoSQL skills, Python, and Azure data services.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering or related field.
• 2–3 years of experience in data engineering or software engineering with data warehousing, big data, cloud technologies and automation.
• Strong data analysis, data verification and problem-solving abilities.
• Analytical, meticulous, and a team player.
• Effective communication across teams.
• Ability to manage multiple tasks in a dynamic environment.
• Self-motivated and eager to learn new skills and technologies.

Responsibilities

• Design, develop, and maintain ETL/ELT pipelines, API endpoints, and data applications across cloud (Azure) and on-prem environments.
• Monitor and optimize data quality, performance, and availability.
• Create and optimize interactive dashboards to visualize business metrics.
• Implement CI/CD pipelines, automate deployments, and ensure scalability for data/ML solutions.
• Collaborate with Data Scientists and engineers to deploy ML/AI projects in production.

Skills

Data analysis

Problem solving

Team player

Communication skills

Multi-tasking

Self-motivated

Education

Bachelor's degree in Computer Science / Computer Engineering

Tools

MS SQL Server

NoSQL databases

Python

Flask

Power BI

Azure Data Factory

CI/CD (Azure DevOps)

Job description

We are seeking a skilled Data Engineer to join the Insights, Digitalization & Analytics department. The ideal candidate will design, develop, and maintain scalable data solutions to drive analytics, machine learning, and business insights. Responsibilities include building data pipelines, APIs, and dashboards, deploying Machine Learning projects, and leveraging big data technologies and cloud platforms.

Responsibilities

• Design, develop, and maintain ETL/ELT pipelines, API endpoints, and data applications across cloud (Microsoft Azure) and on-premise environments, integrating internal and external data sources, including web scraping of public data (ensuring compliance).
• Monitor, optimize, and maintain data quality, performance, and availability through data cleansing, transformation, and deployment of scalable solutions.
• Create and optimize interactive dashboards to visualize business metrics, collaborating with stakeholders to design user-friendly interfaces and integrate them with backend data pipelines.
• Implement CI/CD pipelines, automate deployments, and ensure scalability and performance for data and Machine Learning (ML)/AI solutions.
• Collaborate with Data Scientists and software engineers to deploy, monitor, and maintain ML/AI projects in production systems.

Requirements

• Possess a bachelor's degree in computer science, Computer Engineering, or a related field (specialization in Software Engineering is a plus).
• 2–3 years of experience in data engineering or software engineering with expertise in data warehousing, big data platforms, cloud technologies, and automation tools
• Strong data analysis, data verification and problem-solving abilities.
• Analytical, meticulous, and team player.
• Effective communication skills for collaboration across teams.
• Ability to manage multiple tasks in a dynamic environment.
• Self-motivated and possess initiative to learn new skills and technologies

Technical Skills required:

• Proficiency in relational databases (MS SQL Server), NoSQL, and ETL pipelines using Python or ETL tools (e.g., Microsoft SSIS, Informatica IPC).
• Familiarity with Python web application and API development tools (e.g., Flask, Requests) and web scraping tools (e.g., BeautifulSoup, Scrapy).
• Skilled in Power BI, including DAX and Power Query, for creating reports and dashboards.
• Experience with Microsoft Azure PaaS services, including Azure Data Factory, Data Lake Storage, App Service, and Azure SQL, as well as CI/CD pipelines using Azure DevOps.
• Knowledge of Machine Learning tools (e.g., AutoML platforms like Azure AutoML or DataRobot) and ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch, Keras).
• Familiarity with big data technologies (e.g., Hadoop, Hive, Spark) and Databricks platform (a plus).