Advance the data-driven, evidence-based and citizen-centric approach to health promotion through state-of-the-art data best practices to maximize data quality and internationally-recognised governance and policies to promote safe usage of data.
Overview of Data Operations & Architecture Department
Recognising the efficacy of big data and artificial intelligence in optimising sharper insights and outcomes for public health, the Data Operations and Architecture Department will take a proactive role to drive the design of a 360-view citizen-centric data architecture and the implementation of data operations to support and facilitate an evidence-based, data-driven approach to programme delivery. The department will set up and implement data architecture and structuring, cleaning and validation of big data from diverse HPB and partners’ data sources (e.g. food transaction data, sports attendance data, screening and sensor data) to facilitate data feature engineering use cases pivotal to downstream data exploitation. The department will also be involved in strategic collaborations with partners (e.g. public healthcare and data science institutions, academics and technology players) to bring in their data and expertise onto the data operations to benefit citizens.
We are looking for dynamic and enthusiastic individuals, with strong data engineering and data science skills and project delivery experience, to join us on this pioneering journey to build a more data-driven HPB. You will be pivotal in helping to set up and implement a robust data engineering pipeline that can integrate the vast wealth of data HPB has through its numerous data sources. A robust data pipeline ensures that changes from the data sources does not impact the timely delivery of high quality data to our business users.
Given the diversity of data collected from our lifestyle programmes, data science approaches such as machine learning will also be deployed to support the data engineering process. For example, we have build and included machine learning ops to help with standardising event names.
Responsibilities
- Design optimal data structures for strategic datasets and drive data engineering processes related to data ingestion, data imputation, data transformation and integration to facilitate data science and analytics use cases
- Build optimised, test driven codes to achieve the above mentioned
- Translate research and analytics questions into data features to facilitate dashboarding, model training, analytics, and data science applications
- Build test case on code to ensure that all codes are built to perfection
- Project manage and be the overall lead for engagement to bring together HPB vendors, partners, IT and business teams to plan, set timelines and drive processes that enable timely delivery of the data engineering pipeline guided by business needs
- Lead internal cross-functional teams to integrate, process and engineer disparate datasets into structured and meaningful variables to facilitate machine learning and data science use cases
- Lead the preparation of materials to update senior management periodically on progress of the project and surface issues and challenges for collective deliberation and decision-making
Qualifications and skillsets
- Bachelor/Masters degree in Computer Science, Engineering or Information Systems with major / project experience in data science and/or business analytics from a recognised university. Professional certifications in data engineering from accredited professional bodies will be highly desirable.
- Minimum 3-5 years of relevant project management and delivery experience. Experience in end-to-end deployment of data engineering projects in production environment preferred.
- Proficiency in the use of Python and/or Power BI
- Experience working on data engineering, and/or database architectural work
- Experience working on a few of the following component in the data pipeline: data ETL, data cleaning, data modelling, and feature engineering.
- Able to work in a fast-paced matrix environment, overseeing multiple projects at the same time
- Good communication skills and able to communicate and express complex data/concepts in a clear and simple-to-understand manner
- Have the drive, commitment, and perseverance to do well and have strong intellectual curiosity to learn and seek continuous improvements in the work assigned
- Experience in Big Data technologies e.g. Hadoop and Spark Ecosystems will be desirable
- Experience with test-driven development will be desirable
- Take an interest in digital health and healthcare
