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Main Responsibilities: 

1. Create a reliable and expandable data system that complies with the current and upcoming data requirements of the PSW. Take into account factors like the amount, type, and speed of data.  
2. Work together with cross-functional groups to comprehend business objectives and convert them into practical data architecture solutions. Establish data models, structures, and schema that enable effective data storage, retrieval, and analytics. 
3. Ensure seamless integration of data from various sources while maintaining consistency and accuracy across integrated datasets.  
4. Optimize the Extract, Load, Transform (ELT) processes to efficiently transform and load data into the data warehouse or analytics platform. 
5. It is important to establish security measures for data, such as encryption protocols and access controls, in order to protect sensitive information. It is also crucial to comply with relevant data protection regulations, like GDPR and HIPAA, by implementing appropriate practices for handling data. 
6. Establish and enforce data governance principles to ensure the consistency, quality, and lineage of data. Collaborate with data stewards to define data ownership, data definitions, and data quality standards. 
7. To improve the speed and efficiency of data access, it's important to design efficient data models, create appropriate indexes, and tune queries.  
8. Working with database administrators and performance engineers can help collaborate on enhancing the performance of data retrieval and query processes. 
9. It is important to keep detailed and current records of the data architecture, data flows, data lineage, and data definitions.  
10. To ensure clear communication with stakeholders, it is necessary to provide clear documentation and presentations that explain the benefits of the data architecture. 
11. Develop easy-to-use data structures for efficient dataset access and organization. Collaborate with analysts and scientists to understand their ad-hoc analysis requirements and recommend available data. Assist in creating self-service BI tools and dashboards for user-driven analysis. 
12. Conducting data analysis 
13. Developing data visualizations 
14. Integrating data with other applications 
15. Troubleshooting data problems 
16. Emerging Technology Awareness 
17. Data Availability and Business Continuity Support 
18. Data Lifecycle Management 
19. Training and Knowledge Sharing 
20. Cross-Functional Collaboration 
21. Continuous Improvement and Innovation 
22. BI delivery management with project managers 

Qualification, Experience, and Skills: 

• Bachelor's degree in computer science, or a related field (preferred) 
• Minimum 9+ years of experience in the relevant field. 
• Proven experience in data transformation and visualizations, Oracle technologies are

Skills:

Data Quality, Data Science, SQL, Data Mining, Data Modeling,