Data Engineer - Geylang

As a Data Engineer, you will work closely with data scientists and software engineers to ensure the efficient and reliable flow of data from source systems to analytical tools and platforms.

Responsibilities:

• Design, develop, and maintain scalable data pipelines to ingest, transform, and load clinical study data from various sources, including digital device sensors.
• Optimize data storage and retrieval processes in cloud-based platforms to ensure high performance and reliability.
• Collaborate with data scientists to integrate data processing pipelines with AI-powered algorithms and third-party analytical tools or platforms.
• Implement data quality checks and monitoring mechanisms to ensure the integrity and accuracy of the data.
• Troubleshoot and resolve issues related to data pipeline performance, reliability, and scalability.
• Work closely with software developers, system architects and other cross-functional teams to develop data-driven business solutions.
• Stay up-to-date with emerging technologies in AI & Cloud computing and best practices in data engineering to continuously improve data processing pipelines and infrastructure.

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Proven experience in designing and building data pipelines using ETL tools and frameworks such as Apache Spark, Apache Beam, or Apache Airflow.
• Proficiency in programming languages such as Python, Java, or Scala.
• Strong understanding of database systems, data warehousing concepts, SQL and NoSQL.
• Experience with AI and Cloud Computing: Hands-on experience with cloud platforms like AWS and familiarity with AI solutions in these environments.
• Excellent problem-solving and troubleshooting skills with a strong attention to detail and quality.
• Effective communication and collaboration skills with the ability to work in a team environment.

Preferred Qualifications:

• Experience with containerization and orchestration tools such as Docker and Kubernetes.
• Familiarity with big data technologies such as Hadoop, Hive, or Presto.
• Knowledge of distributed computing frameworks such as Apache Hadoop or Apache Spark.
• Familiarity with ElasticSearch or AWS OpenSearch is plus
• Prior experience working with healthcare or clinical data is a plus.