Data Engineer

Requirements

Essential

Minimum 3 years of experience in data engineering, analytics engineering, orbusiness 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

Key Responsibilities

Data Modelling and Transformation

Design and maintain data models that unify cyber asset information from diverse

Build and maintain transformation pipelines that clean, normalise, enrich, andrelate ingested data into a coherent asset inventory

Establish and enforce data quality standards deduplication, completenesschecks, schema validation, and lineage tracking

Evolve the data model as new data sources are onboarded, ensuring backwardcompatibility 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