Do you love healthtech and Singapore put together? Then you just might love IHiS.
IHiS is a multiple award-winning Healthcare IT Leader that digitises, connects, and analyses Singapore's health ecosystem. Its ultimate aim is to improve the Singapore population's health and health administrations by integrating intelligent, highly resilient, and cost effective technologies with process and people.
We’re looking for a highly-driven and motivated Solution Architects (Data Analytics) for our Data aNalytics & Ai (DNA) team in Singapore.
If you are looking for a dynamic environment and to work with projects that use data to make a difference in our public healthcare system, this is the right place for you.
Role & Responsibilities
- Work on analysis of health care information obtained various source systems.
- Provide input to the development, maintenance and promulgation of the blue prints, roadmaps and reference architectures for Data Analytics infrastructure and services.
- Perform analysis of new requirements and develop solutions, and manage solution delivery through acquisition or change control
- Revise existing design specifications and development of new specifications.
- Manage the development of queries to satisfy functional requirements.
- Manage the development of analytical artefacts, including dashboards, reports and graphs as required.
- Provide advice on high-level Business Intelligence, Data warehouse, Data Lake or Advanced Analytics solution designs.
- Evaluate technologies/services, providing regular reporting on emerging trends, value add information and potential impact to the healthcare landscape.
- Proactively achieve the IT strategic, broader business and IT objectives through influencing stakeholder outcomes.
- Assist in vendor management to ensure contracted vendors deliver solutions that is architecturally sustainable and scalable.
- Degree/Master in Computer Science, Information Technology, Computer Engineering or equivalent.
- Minimum eight (8) years’ experience in providing data warehouse or advanced analytics solutions.
- Experience with
- databases (e.g. Oracle, DB2, MS SQL, MySQL, Teradata, Greenplum)
- data repository design (e.g. operational data stores, dimensional data stores, data marts)
- 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.
- Experience in data modelling for analytics (e.g. star schemas, snowflake schemas).
- 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).
- Experience with data acquisition tools (e.g., ETL, real-time data capture, and change data capture).
- Comfortable working independently to carry out data analysis, estimate data quality and sufficiency. Understanding and analysing huge volumes of data drawn from heterogeneous sources / repositories.
- Exposure to the development, evolution and adherence to the architectures relevant to the domain.
- Experience with Business Intelligence Tools (e.g. SAS, SPSS, Cognos, Microstrategy, Microsoft analysis services, Business Objects).
- Advance knowledge of Excel features and techniques required for the presentation of data.
- Ability to work with a wide variety of report generation software (e.g. SSRS, Crystal reports) and generate a wide variety of outputs (dashboards, scheduled reports).
- The ability to work towards strict and conflicting deadlines, be able to plan and prioritise in an environment with multiple stakeholders.
- Good interpersonal skills, a detail-oriented & flexible person who can work across different areas within the team.
- A good understanding of Singapore Healthcare System.
- Familiarity or experience with health informatics would be preferred.
- An understanding of healthcare data governance, data acquisition and data management would be an advantage.
- Experience in interacting with analytics stakeholders (economists, statisticians, clinicians, policy makers) on a business or domain level would be preferred.
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