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Data Engineer

Posted on March 11, 2025

Bukit Timah, Singapore
0 - 0 USD (yearly)

Full Time

Job Title: Data Engineer
Location: Singapore
Client: Bounteous - UOB
Experience: 5+ years
Employment Type: Full-time

Who We Are:

Enterprise Analytics (EA) stands at the forefront of UOB's vision, aiming to establish the bank as a leader in deploying cutting-edge Analytics and Machine Learning models. Comprising a team of experts, we focus on enhancing the bank's capabilities in supporting key areas such as Risk, Wealth, Retail, etc., with a strong emphasis on coordination in data management, technology, and internal stakeholder collaboration.

Upholding UOB’s core value of being enterprising, we are incredibly thrilled about strengthening this strategic unit’s capabilities for the bank, envisioning significant progress and innovation. We firmly believe that advanced analytics is a pivotal factor in delivering premier AI services to our stakeholders.

Job Description:

We are looking for a highly skilled Data Engineer to join our team for our esteemed client Bounteous - UOB in Singapore. The ideal candidate will have 6+ years of experience in data engineering and strong expertise in Java, Scala, Python, Hadoop, Spark, OpenShift, and DevOps. This role requires hands-on experience in building scalable data pipelines, optimizing data workflows, and ensuring data quality and integrity.

Key Responsibilities:

Implement data transformation, aggregation, and enrichment processes to support various data analytics and machine learning initiatives.
Collaborate with cross-functional teams to understand data requirements and translate them into effective data engineering solutions.
Ensure data quality and integrity throughout the data processing lifecycle.
Design and deploy data engineering solutions on OpenShift Container Platform (OCP) using containerization and orchestration techniques.
Optimize data engineering workflows for containerized deployment and efficient resource utilization.
Develop and deploy data solutions using Java, Scala, and Python.
Work with big data frameworks like Apache Spark, Hadoop, or Kafka to process and transform large datasets.
Collaborate with DevOps teams to streamline deployment processes, implement CI/CD pipelines, and ensure platform stability.
Implement data governance practices, data lineage, and metadata management to ensure data accuracy, traceability, and compliance.
Monitor and optimize data pipeline performance, troubleshoot issues, and implement necessary enhancements.
Implement monitoring and logging mechanisms to ensure the health, availability, and performance of the data infrastructure.
Document data engineering processes, workflows, and infrastructure configurations for knowledge sharing and reference.
Stay updated with emerging technologies, industry trends, and best practices in data engineering and DevOps.
Provide technical leadership, mentorship, and guidance to junior team members to foster a culture of continuous learning and innovation.

Required Skills & Qualifications:

Bachelor's degree in Computer Science, Information Technology, or a related field.
Proven experience as a Data Engineer, working with Hadoop, Spark, and data processing technologies in large-scale environments.
Strong expertise in designing and developing data infrastructure using Hadoop, Spark, and related tools (HDFS, Hive, Pig, etc.).
Experience with containerization platforms such as OpenShift Container Platform (OCP) and container orchestration using Kubernetes.
Proficiency in programming languages commonly used in data engineering, such as Java, Scala, Python, and Spark.
Knowledge of DevOps practices, CI/CD pipelines, and infrastructure automation tools (e.g., Docker, Jenkins, Ansible, BitBucket).
Experience with Grafana, Prometheus, and Splunk will be an added benefit.
Strong problem-solving and troubleshooting skills with a proactive approach to resolving technical challenges.
Excellent collaboration and communication skills to work effectively with cross-functional teams.
Ability to manage multiple priorities, meet deadlines, and deliver high-quality results in a fast-paced environment.
Experience with cloud platforms (AWS, Azure, GCP) and their data services is a plus.