G13 - Data Engineer

Overview

In this role, you will transform raw cyber asset data into structured, actionable intelligence that helps government agencies improve asset visibility, prioritize vulnerabilities, and strengthen cybersecurity operations.

You will design scalable data models, build transformation pipelines, and develop dashboards that enable stakeholders to make informed decisions based on accurate and timely data.

The Asset Intelligence Platform is a strategic initiative focused on reducing cybersecurity risks by improving visibility across a federated IT environment and enabling effective threat prioritization and remediation tracking.

Responsibilities

Data Engineering & Modelling

• Design and maintain scalable data models that consolidate cyber asset information from multiple sources, including CMDBs, vulnerability scanners, endpoint platforms, and agency-specific systems.
• Build and maintain data transformation pipelines to clean, normalize, enrich, and relate ingested data into a unified asset inventory.
• Implement and enforce data quality controls, including validation, deduplication, completeness checks, schema validation, and lineage tracking.
• Continuously evolve data models as new data sources are onboarded while maintaining compatibility with existing datasets and reporting solutions.

Dashboard & Analytics Development

• Design and develop dashboards supporting asset visibility, vulnerability management, patch tracking, and incident response readiness.
• Collaborate with stakeholders to define meaningful metrics, KPIs, and visualizations that drive operational decision-making.
• Translate complex datasets into intuitive dashboards and actionable insights.
• Enhance and maintain existing reporting solutions based on evolving business and operational requirements.

Data Platform Operations

• Validate data ingestion processes to ensure completeness, accuracy, freshness, and schema compliance.
• Monitor pipeline health and proactively investigate data anomalies or quality issues.
• Optimize query performance, data refresh schedules, and dashboard responsiveness.
• Maintain documentation for data models, transformation logic, pipelines, and reporting assets.

Stakeholder Collaboration & Insights

• Partner with business stakeholders to identify trends, risks, and opportunities within cyber asset datasets.
• Provide technical guidance on new reporting requirements and data-driven use cases.
• Contribute to the development of asset visibility metrics, coverage indicators, quality scores, and operational benchmarks.
• Work closely with cross-functional teams including infrastructure engineers, cybersecurity specialists, and business analysts.

Requirements

Essential

• Minimum 3 years of experience in Data Engineering, Analytics Engineering, Business Intelligence, or related fields.
• Strong SQL skills and hands-on experience with data transformation frameworks such as dbt, Apache Spark, or equivalent technologies.
• Experience developing dashboards and visualizations using tools such as Power BI, Tableau, Grafana, Superset, or similar platforms.
• Strong understanding of data modelling concepts, including dimensional modelling, entity resolution, and analytical data structures.
• Experience working with large, heterogeneous datasets from multiple sources.
• Familiarity with data quality management practices including validation, monitoring, lineage tracking, and deduplication.
• Strong analytical and problem-solving skills with attention to detail.

Preferred

• Experience working with cybersecurity-related datasets such as CMDBs, vulnerability management platforms, endpoint management systems, or network discovery tools.
• Familiarity with tools such as Qualys, Tenable, Rapid7, or similar cybersecurity platforms.
• Understanding of attack surface management concepts, asset ownership, vulnerability lifecycle management, and exposure assessment.
• Experience with cloud platforms and enterprise environments.
• Knowledge of data orchestration tools such as Airflow, Dagster, or Prefect.
• Experience using Python for data processing, automation, or analytics.
• Experience working within cross-functional teams involving infrastructure, security, and business stakeholders.

Key Competencies

• Analytical Thinking – Ability to transform complex and fragmented datasets into meaningful insights.
• Data Quality Mindset – Strong focus on accuracy, consistency, and reliability of data assets.
• Business Impact Orientation – Understands how data supports operational and strategic decision-making.
• Collaboration – Works effectively with technical and non-technical stakeholders.
• Adaptability – Comfortable operating in evolving environments with diverse data sources and requirements.
• Communication – Able to explain technical concepts, data models, and insights clearly to stakeholders at all levels.

Experience Level: 3+ Years

• Role Type: Data Engineering / Analytics Engineering / Business Intelligence
• Domain: Cybersecurity, Asset Intelligence, Data Analytics, Government Technology