A role is to design, build, and manage data warehouse solutions. Possess the ability to utilize ETL tools, integrate, and optimize data to enhance the efficiency of processes and data flows.
KEY RESPONSIBILITIES:
- Utilize expertise in life insurance, finance, and current system functionalities to operate and develop the Data Warehouse system.
- Design and implement a scalable and reliable data pipeline to ingest, transform, and load structured and unstructured data from various sources
- Design, build, and manage Data Warehouse solutions to support business analysis and reporting needs.
- Database Management: Utilize expertise in databases such as Oracle, Postgres, MySQL to optimize data storage, retrieval, and processing (SAP Hana is an advantage)
- ETL Tools: Deploy and manage ETL pipelines using tools such as IBM DataStage, Oracle ODI, and Oracle OBIEE.
- Data Integration: Collaborate with other teams to integrate diverse data sources into centralized platforms.
- Data Pipeline Optimization: Ensure high performance, scalability, and reliability of data processes and pipelines.
- Data Governance: Establish and adhere to data quality standards, ensuring compliance with industry and organizational best practices. Monitor and audit data to ensure accuracy, completeness, and reliability
- Implement monitoring, logging, and alerting for data pipelines to ensure high availability and early detection of failures
- Problem Solving: Effectively address and resolve issues in data processes and pipelines.
- Strong understanding of database management, data modeling, and data integration techniques.
- Proficiency in scripting languages, query optimization, and developing packages, procedures, and functions on Oracle is an advantage.
- Knowledge of big data technologies and cloud platforms such as AWS is an advantage.
- Design, build, and maintain data pipelines to support training and deployment of AI/ML models in software and hardware IT projects.
- Research and deploy AI technologies to optimize data processing performance, enhance data quality management, and detect anomalies within large-scale data systems.
