Data Engineer

at  Quess Corp Limited

JOB INFORMATION

Salary
6000
Industry
Technology
Date Opened
10/21/2024
Job Type
Contract
State/Province
Singapore
City
Singapore
Zip/Postal Code
048616
Country
Singapore

YOUR PROFILE

We are seeking a skilled and motivated Data Engineer with expertise in Hadoop, Spark, OpenShift Container Platform (OCP), and DevOps practices. As a Data Engineer, you will be responsible for designing, developing, and maintaining efficient data pipelines, processing large-scale datasets. Your expertise in Hadoop, Spark, OCP, and DevOps will be crucial in ensuring the availability, scalability, and reliability of our ML Solutions.

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
• 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 to the continuous improvement of the analytics capabilities within the bank

REQUIREMENTS

Requirements:

• 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 Spark, Python, Scala, or Java
• Knowledge of DevOps practices, CI/CD pipelines, and infrastructure automation tools (e.g., Docker, Jenkins, Ansible, BitBucket)
• Experience with Graphana, Prometheus, 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 (e.g., AWS, Azure, GCP) and their data services is a plus

• 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
• 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 to the continuous improvement of the analytics capabilities within the ban