WEBSPARKS PTE. LTD. Hiring! Full Time Data Engineer (Public Healthcare) in Islandwide (Singapore), Earn up to SGD 6,000 - Ricebowl

• 6-month contract, renewable
• Government project
• Hybrid work arrangement

We are seeking one Data Engineer to establish and maintain the data infrastructure required to integrate, process, govern and use these data reliably. The data engineer will support the development of scalable data pipelines, standardised data models, secure data environments and high-quality datasets for product analytics, programme evaluation, research and AI development. This capability is necessary to enable evidence-informed decision-making, measure health and operational outcomes, support responsible AI deployment, and facilitate the future scaling and integration of mental health innovations across the healthcare ecosystem.

1. Data Architecture and Strategy

• Design and maintain scalable data architectures to support digital mental health platforms, analytics, research, and AI-enabled use cases.
• Translate programme, product, research, and operational requirements into data architecture, data flow, storage, processing, and integration requirements.
• Develop target-state and transitional data architecture plans aligned with system roadmaps, security requirements, and anticipated data volumes.
• Recommend appropriate data engineering approaches, technologies, and design patterns based on performance, cost, maintainability, interoperability, and security considerations.
• Ensure data architecture supports future scaling, cross-system integration, advanced analytics, machine learning, and responsible data reuse.

2. Data Pipeline Development and Integration

• Design, develop, test, deploy, and maintain batch and real-time data pipelines across relevant source systems.
• Extract, transform, and load data from digital platforms, applications, clinical systems, surveys, research tools, third-party services, and other approved data sources.
• Integrate structured, semi-structured, and unstructured data, including user interaction data, assessment results, system logs, conversational data, operational data, and AI-generated outputs.
• Develop and maintain application programming interfaces, connectors, data ingestion services, and data exchange mechanisms.
• Ensure pipelines are reliable, modular, reusable, scalable, and capable of handling changes in source data structures.
• Implement appropriate error handling, retry logic, reconciliation processes, and failure notifications.

3. Data Modelling and Storage

• Design and maintain logical and physical data models, schemas, data marts, and analytical datasets.
• Develop standardised data structures and common definitions across programmes, products, and use cases.
• Establish appropriate relationships between user, session, assessment, intervention, engagement, referral, escalation, provider, and outcome data.
• Optimise data storage and query performance for operational reporting, research analysis, product analytics, and machine learning workloads.
• Implement data partitioning, indexing, retention, archival, and deletion mechanisms where required.
• Maintain clear separation between raw, processed, curated, and consumption-ready data layers.

4. Data Quality, Validation and Observability

• Define and implement automated data quality checks covering completeness, accuracy, validity, consistency, uniqueness, timeliness, and referential integrity.
• Establish data validation rules and acceptance thresholds in consultation with product, research, analytics, clinical, and operational teams.
• Develop monitoring dashboards and alerts for pipeline failures, delayed data, schema changes, anomalous values, missing records, and data drift.
• Investigate and resolve data quality issues, including root-cause analysis and corrective action.
• Maintain data quality logs, issue registers, reconciliation reports, and resolution records.
• Support validation of key metrics to ensure consistency between source systems, analytical datasets, and reporting outputs.

5. Data Governance, Privacy and Security

• Implement data engineering controls in accordance with applicable data protection, cybersecurity, healthcare, research, and organisational requirements.
• Apply appropriate access controls, encryption, masking, pseudonymisation, anonymisation, tokenisation, and segregation of sensitive data.
• Ensure personal, health-related, research, and conversational data are handled according to approved purposes and access permissions.
• Maintain data lineage, data provenance, processing records, and traceability across data pipelines and systems.
• Support implementation of data retention, archival, disposal, consent, and purpose-limitation requirements.
• Ensure datasets used for analytics and AI development are properly versioned, documented, and auditable.
• Support privacy impact assessments, security reviews, risk assessments, audits, and compliance reviews.

6. Analytics and AI Data Enablement

• Develop curated datasets and feature-ready data pipelines to support analytics, evaluation, machine learning, and AI-enabled applications.
• Work with data scientists, researchers, product managers, clinicians, and engineers to define data requirements for model development, validation, deployment, and monitoring.
• Prepare training, validation, test, and monitoring datasets using reproducible and documented processes.
• Support feature engineering, labelling workflows, dataset versioning, and metadata management.
• Implement pipelines for monitoring model inputs, outputs, performance, drift, bias indicators, latency, and safety-related events.
• Ensure AI-related datasets retain appropriate traceability to source data, processing rules, model versions, and deployment environments.
• Support retrieval, knowledge-base, vector database, and other data infrastructure required for generative AI or retrieval-augmented systems, where applicable.

7. Reporting, Metrics and Research Support

• Develop and maintain datasets required for operational dashboards, product analytics, programme evaluation, and research studies.
• Work with stakeholders to operationalise agreed definitions for engagement, completion, retention, referral, escalation, clinical outcomes, service utilisation, and productivity measures.
• Support longitudinal analysis, cohort tracking, funnel analysis, and linkage of user activity with programme or outcome data.
• Develop reproducible data extraction and transformation processes for research and evaluation purposes.
• Support preparation of de-identified or anonymised datasets for approved research partners.
• Ensure reporting datasets are refreshed according to agreed schedules and service levels.
• Investigate discrepancies in reports, dashboards, research extracts, and management information.

8. Platform Operations and Performance

• Monitor and maintain the performance, reliability, availability, and cost efficiency of data infrastructure and pipelines.
• Optimise data processing jobs, storage utilisation, query performance, and cloud resource consumption.
• Implement appropriate logging, monitoring, backup, recovery, and disaster recovery arrangements.
• Support incident management for data pipeline, storage, integration, and reporting failures.
• Conduct root-cause analysis and implement preventive measures following incidents.
• Manage data engineering deployments across development, testing, staging, and production environments.
• Support infrastructure-as-code, automated testing, continuous integration, and continuous deployment practices where applicable.

9. Delivery and Cross-Functional Coordination

• Participate in sprint planning, backlog refinement, technical design reviews, and delivery discussions.
• Translate business and technical requirements into data engineering tasks, specifications, and acceptance criteria.
• Coordinate with product, software engineering, cloud infrastructure, cybersecurity, analytics, data science, research, clinical, and operational teams.
• Identify data dependencies, integration risks, technical constraints, and sequencing requirements.
• Support system integration testing, user acceptance testing, performance testing, and production release readiness.
• Provide regular progress updates, risk reports, issue logs, and mitigation plans.
• Coordinate with third-party vendors and system owners where data integration depends on external platforms or services.

10. Documentation, Knowledge Transfer and Transition

• Maintain comprehensive documentation covering data architecture, schemas, data dictionaries, lineage, pipelines, interfaces, transformation logic, quality rules, and operational procedures.
• Document source-to-target mappings, data definitions, business rules, dependencies, schedules, ownership, and access controls.
• Maintain deployment guides, troubleshooting guides, runbooks, recovery procedures, and incident response procedures.
• Ensure code, configurations, scripts, credentials references, and technical artefacts are maintained in approved repositories..
• Provide updated documentation and transition artefacts before completion or termination of the engagement.

11. Testing and Technical Assurance

• Develop and execute unit, integration, regression, reconciliation, performance, and data quality tests.
• Implement automated testing for data transformations, schemas, interfaces, and pipeline dependencies.
• Validate that data pipelines meet agreed functional and non-functional requirements.
• Support vulnerability assessments, penetration testing, code reviews, architecture reviews, and technical assurance activities where relevant.
• Resolve defects according to agreed severity levels and service timelines.
• Maintain test evidence, defect records, release notes, and remediation documentation.