GovTech Data Engineer

We are seeking a Data Engineer to join the Asset Intelligence Platform (AIP) team. The successful candidate will be responsible for transforming raw ingested cyber asset data into structured, query-able models and building the dashboards and visualizations that enable government agencies to understand their attack surface, prioritize vulnerabilities, and respond to incidents. This role is critical to achieving AIP's mission of reducing the government's attack surface by turning data connectivity into actionable intelligence. The Asset Intelligence Platform is a collaboration between the Cybersecurity Group (CSG), Government IT Security Incident Response (GITSIR), and Government Productivity Engineering (GPE). The platform addresses critical gaps in the government's federated IT environment specifically the information collection gap and vulnerability scanner gap to enable effective threat prioritization and remediation tracking. The Data Engineer operates at the intersection of platform infrastructure and agency value receiving ingested data from the Platform Infrastructure Engineer and shaping it into insights that the Business Analyst delivers to agencies. This role owns the data layer between raw ingestion and decision-ready output. Key Responsibilities Data Modelling and Transformation Design and maintain data models that unify cyber asset information from diverse sources (WOG central systems, agency-specific data sources, vulnerability scanners, CMDBs) Build and maintain transformation pipelines that clean, normalise, enrich, and relate ingested data into a coherent asset inventory Establish and enforce data quality standards deduplication, completeness checks, schema validation, and lineage tracking Evolve the data model as new data sources are onboarded, ensuring backward compatibility and minimal disruption to existing dashboards Dashboard and Visualisation Development Build dashboards that address agency-specific use cases including asset visibility, vulnerability prioritisation, patch tracking, and incident response readiness

Collaborate with the Business Analyst to develop compelling data narratives selecting the right metrics, views, and drill-downs that connect data to agency decision-making Iterate on dashboard designs based on agency feedback, balancing clarity with analytical depth Maintain and update existing dashboards as underlying data models or agency requirements evolve Platform Data Operations Validate successful data ingestion in coordination with the Platform Infrastructure Engineer confirming completeness, freshness, and schema conformance Monitor data pipeline health, investigate anomalies, and resolve data quality issues Optimise query performance and data refresh schedules to ensure dashboards remain responsive and current Document data models, transformation logic, and dashboard specifications for operational continuity Insights and Collaboration Partner with the Business Analyst to identify patterns and insights within ingested data that support agency engagement Provide technical input on feasibility and effort when new agency use cases are proposed Contribute to defining what "good" looks like for asset visibility coverage metrics, quality scores, and completeness indicators Requirements Essential Minimum 3 years of experience in data engineering, analytics engineering, or business intelligence development Strong proficiency in SQL and experience with data transformation frameworks (e.g., dbt, Apache Spark, or equivalent) Hands-on experience building dashboards and visualizations with BI tools (e.g., Power BI, Tableau, Grafana, Superset, or platform-native tooling) Experience designing data models for operational or analytical use cases star schemas, entity resolution, or graph-based asset relationships

Ability to work with messy, heterogeneous data from multiple sources and produce clean, reliable outputs Understanding of data quality practices including validation, deduplication, lineage, and monitoring Desirable Experience with cyber asset data CMDBs, vulnerability scanners (Qualys, Tenable, Rapid7), endpoint management platforms, or network discovery tools Familiarity with attack surface management concepts including asset ownership, exposure scoring, and vulnerability lifecycle Experience with the Singapore government IT landscape, GCC (Government Commercial Cloud), and WOG shared services Familiarity with data pipeline orchestration tools (e.g., Airflow, Dagster, Prefect) Experience with Python for data manipulation and automation Prior experience in cross-functional teams working alongside infrastructure engineers and business analysts Competencies Analytical Rigor: Ability to make sense of complex, heterogeneous data and produce models that are both correct and useful Outcome Orientation: Focuses on delivering insights that drive agency action, not just technically correct outputs Collaboration: Effective at working across disciplines partnering with Business Analysts on storytelling and Platform Infrastructure Engineers on data ingestion Adaptability: Comfortable working with imperfect data from diverse agency environments and iterating toward progressively better coverage and quality Communication: Able to explain data models, quality trade-offs, and dashboard logic to non-technical stakeholders