Data Engineer

Аналитика рынка

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Описание

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 , 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

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