Hiring For Solutions Architect-Data Analytics -Onsite Singapore

1. Degree/masters in computer science, Information Technology, Computer Engineering or equivalent.
2. Minimum eight (8) years’ experience in providing data warehouse or advanced analytics solutions.
3. Experience with

• databases (e.g. Oracle, DB2, MS SQL, MySQL, Teradata, Databricks)
• data repository design (e.g. operational data stores, dimensional data stores, data marts, datalakes)
• data interrogation techniques (e.g. SQL, NoSQL).
• structured and unstructured data analytics.
• statistical computing programming language (e.g. R, Python).
• data quality tools and processes.
• data transformation and terminology equivalence mapping.

1. Experience in data modelling for analytics (e.g. star schemas, snowflake schemas).
2. Deep understanding of analytical models and methodologies – especially in the context of health analytics for clinical use and clinical safety (e.g. data mining, predictive analytics).
3. Experience with data acquisition tools (e.g., ETL, real-time data capture, and change data capture).
4. Thorough understanding of ML, data analysis, data visualization, and event-driven architecture.
5. Exposure to the development, evolution and adherence to the architectures relevant to the domain.
6. Ability to work with a wide variety of report generation software (e.g. Power BI, Tableau and OBIEE) and generate a wide variety of outputs (dashboards, scheduled reports).
7. The ability to work towards strict and conflicting deadlines, be able to plan and prioritise in an environment with multiple stakeholders.
8. Good interpersonal skills, a detail-oriented & flexible person who can work across different areas within the team.
9. Familiarity or experience with health informatics would be preferred.
10. An understanding of healthcare data governance, data acquisition and data management would be an advantage.
11. Experience in interacting with analytics stakeholders (economists, statisticians, clinicians, policy makers) on a business or domain level would be preferred.

1.1.1 Cloud Exposure (AWS):

1. Strong knowledge and practical experience with AWS.
2. Familiarity with modern cloud application architecture.
3. Exposure to cloud managed services and microservices like Function as a Service, Containers, and managed databases.
4. Experience with setting up load balancers, cloud networks, and virtual servers.
5. Infrastructure as code (IAC): Cloud formation, terraform.
6. AWS identity and access management, integration with active directory.
7. Familiarity with security and access control measures to control privileged access to test and production environment.
8. Certification in AWS Cloud Practitioner or AWS Architect.