Data Engineer [Public Healthcare]

• 10-month contract, renewable • Hybrid work arrangement • Government project Project OverviewAs the platform expands beyond specialist settings into general psychiatry and care coordination workflows, it has demonstrated both clinical value and the need for greater scalability, flexibility across care models, and stronger system integration to support higher patient volumes and broader population use. In response, the platform is evolving from a single clinical product into a scalable, modular digital phenotyping platform. This platform adopts a layered approach to user engagement and data collection, ranging from low-touch, privacy-first mobile engagement with minimal data collection, to opt-in digital phenotyping using smartphone and wearable data, and up to fully integrated, clinician-supported care models. This tiered design enables safe, large-scale adoption at lower layers while preserving clinical rigour and validated use in higher-acuity settings. It also supports Bring Your Own Device (BYOD) models and lightweight mobile-only implementations, improving accessibility and cost efficiency. A key feature of this platformisation is the ability to dynamically adjust a user's level of participation based on clinical need, risk signals, and user consent—enabling seamless transitions between self-guided and clinician-supported care. Supported by ongoing efforts such as Healthcare Commercial Cloud (HCC) migration and integration with platforms (e.g., mindline.sg), the platform is positioning itself as a foundational, privacy-first digital phenotyping service that can power multiple mental health use cases across MOHT, from population wellbeing to high-acuity clinical care. Responsibilities• Build scalable, privacy-preserving data pipelines that integrate heterogeneous signals (e.g., phone sensors, BYOD wearables, assigned devices, self-report) across platform layers L1–L4, ensuring reliability, transparency, and safe downstream use • Design and maintain ETL/ELT pipelines for multi-layer data ingestion (L1: minimal data to L4: high-trust clinically validated streams) using Python, Pandas, NumPy, Bash scripting, SQL, AWS, and AWS Lambda • Implement data quality checks, confidence labeling, and variance-handling mechanisms for BYOD devices (e.g., steps, sleep, resting heart rate) • Develop data governance rules aligned with the layered consent model (e.g., opt-in, pause, withdrawal, data minimization) • Create scalable feature stores for wellbeing insights, descriptive summaries, and clinician-facing analytics • Integrate structured and semi-structured signals (e.g., sleep patterns, routines, check-ins, activity–mood correlations) • Support privacy-first processing, including pseudonymization, access controls, audit logging, and tier-specific visibility • Collaborate with ML and analytics teams to deliver safe, non-diagnostic insights and transparent uncertainty explanations Requirements• Bachelor’s degree in Computer Science, Data Engineering, or a related field • Minimum 4 years of experience in data engineering or related roles • Strong programming skills in Python, with experience using data processing libraries such as Pandas and NumPy • Proficiency in SQL and experience working with relational and/or distributed data systems • Experience building and maintaining ETL/ELT pipelines and data workflows • Familiarity with cloud platforms (e.g., AWS) and services such as AWS Lambda • Understanding of data modeling, data quality, and pipeline reliability best practices • Experience working with semi-structured data and integrating multiple data sources • Awareness of data privacy and governance principles, especially in handling sensitive data • Strong collaboration skills to work with cross-functional teams (e.g., ML, analytics, DevOps)