Mô tả công việc
We are looking for Data Engineers to join 02 dynamic teams at Home Credit: IT and CRM Technology
IT Team:
Design and develop dbt to deliver of analytical data products.
Design and manage the Enterprise Dimensional Model for Home Credit data.
Provide data modeling expertise to all Home Credit data teams through code reviews, pairing, and training to help deliver optimal, DRY, reusable, and scalable data designs and queries in Big Data Platform.
Implement the DataOps philosophy in everything you do.
Serve as data model subject matter expert, approve any data model changes for our analytical data products.
CRM Technology Team:
Technical Development:
Non- Production Development: Develop and test SAS solutions in non- production environments, adhering to best practices and coding standards.
CRM Component Development: Develop, test, and deploy SAS jobs, processes, and web services to support marketing campaigns and decisioning strategies.
Production Deployment: Collaborate with the IT team to deploy approved SAS solutions to production environments, ensuring smooth transitions and minimal disruptions.
Data Mart Management: Manage and maintain the SAS Data Mart, including data quality, ETL processes, and data security.
Integration and Performance Optimization: Integrate CRM SAS solutions with internal systems and cloud platforms, ensuring optimal performance and data flow.
Configuration and Customization: Configure and customize SAS applications, including information maps, business contexts, campaign definitions, response definitions, common data models, treatments, and decision solutions.
System architecture:
Architectural Design: Participate in cross- functional teams to define and enhance the overall SAS architecture, focusing on scalability, performance, and maintainability.
Best Practice Development: Establish and promote best practices for SAS development, including coding standards, documentation, and performance tuning.
Solution Design: Design and document complex SAS solutions, considering factors such as data flow, processing logic, and performance requirements.