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