Data Engineer

PSA Corporation; Singapore; On-site; SGD 70,000 - 90,000; Full time

Job summary

A leading data solutions provider in Singapore is looking for a skilled Data Engineer to join their Insights, Digitalization & Analytics department. In this role, you will design, develop, and maintain data solutions, focusing on ETL processes and leveraging Azure cloud services. Ideal candidates will have a strong background in data engineering, effective problem-solving capabilities, and the ability to collaborate with diverse teams to deliver innovative analytics solutions.

Qualifications

• 2–3 years of experience in data engineering or software engineering.
• Strong data analysis and verification skills.
• Self-motivated with a willingness to learn new technologies.

Responsibilities

• Design and maintain ETL/ELT pipelines and API endpoints.
• Monitor and optimize data quality and performance.
• Collaborate with Data Scientists on ML projects.

Skills

Data warehousing

Big data platforms

Data analysis

Cloud technologies

ETL pipelines

Collaboration

Problem-solving

Education

Bachelor's degree in Computer Science or related field

Tools

Microsoft SQL Server

Python

Power BI

Azure Data Factory

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 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.
• Design, implement, and manage Azure cloud infrastructure to support scalable and secure deployment of cloud-native applications, including configuration of networking, storage, compute resources, and identity management. Ensure alignment with best practices for performance, cost optimization, and security compliance.
• Implement CI/CD pipelines, automate deployments, and ensure scalability and performance for data and Machine Learning (ML)/AI 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.
• Collaborate with Data Scientists 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 data warehouse design including relational databases (MS SQL Server), NoSQL, and ETL pipelines using Python or ETL tools (e.g., Microsoft SSIS, Informatica IPC) and data warehousing concepts, database optimization, and data governance.
• 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 architecting and implementing Microsoft Azure 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.