Data Engineer

at  OPENSOURCE TECHNOLOGIES PTE LTD

Roles & Responsibilities

• Based in the Group Customer Analytics & Decisioning team, you will be responsible for developing & optimizing CFS related data pipelines & architecture
• Create and maintain the optimal data pipeline to enable ingestion from a wide variety of structured and unstructured data sources
• Responsible for UAT execution with a focus on strategic testing processes and procedures to ensure that business standards and specifications are met
• Support various business functions by collaborating with multiple stakeholders – business, IT, and vendors independently
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery and cleansing/transformation
• Support development and deployment of applications utilising the data pipelines to provide actionable insights

REQUIREMENTS:

• Minimum 5 years of working experience in data management, ETL and analysis functions
• Solid background in traditional structured database environments such as Teradata / Oracle, SQL & PL/SQL
• Knowledge on data warehouse and FSLDM concepts
• Experience in automation process – building procedures, ETL and automated job scheduling – using the likes of DataStage, Talend, etc.
• Exposure to basic Hadoop Ecosystem components such as HIVE / HDFS
• Understanding of banking with exposure to consumer business functions is preferred

• Based in the Group Customer Analytics & Decisioning team, you will be responsible for developing & optimizing CFS related data pipelines & architecture
• Create and maintain the optimal data pipeline to enable ingestion from a wide variety of structured and unstructured data sources
• Responsible for UAT execution with a focus on strategic testing processes and procedures to ensure that business standards and specifications are met
• Support various business functions by collaborating with multiple stakeholders – business, IT, and vendors independently
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery and cleansing/transformation
• Support development and deployment of applications utilising the data pipelines to provide actionable insight