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