Hybrid: Principal Data Engineer

Job description

• Partner with Cybersecurity leaders, risk stakeholders, and non-Cyber teams to define and deliver data-driven Cyber use cases, aligned to enterprise risk priorities and frameworks (e.g., NIST CSF).
• Leverage scalable data pipelines, models, and architectures that enable Cyber analytics, AI, reporting, and advanced use cases across vulnerability management, threat exposure, control effectiveness, and risk insights.
• Work directly with data owners and platform teams to ingest, transform, normalize, and model security and IT datasets, ensuring data quality, lineage, and trust.
• Develop and operationalize analytics products including executive dashboards, strategic metrics, and operational reporting for leadership, governance forums, and front-line Cyber teams.
• Prototype and productionize integrations across Cyber tools and enterprise data platforms, partnering closely with data engineering and architecture teams to ensure sustainability, performance, and supportability.
• Apply advanced analytics, data modeling, and automation techniques to translate raw Cyber telemetry into actionable outcomes, risk indicators, and decision support.
• Leverage AI-assisted development and analytics workflows (e.g., Claude, code-generation tools, AI-augmented analysis) to accelerate engineering, insight generation, and experimentation while operating within established security and data governance controls.
• Translate complex technical findings into clear, consumable narratives for executive and non-technical stakeholders, connecting analytics outputs directly to Cyber risk, business impact, and outcomes.
• Serve as a thought partner and technical advisor, helping shape the Cyber data strategy, architecture direction, and future-state analytics capabilities.

Skills: Minimum Qualifications

• Bachelor s degree in a relevant field (e.g., Computer Science, Data Engineering, Analytics, Information Security, or equivalent experience).
• 8+ years of experience working in Cybersecurity, risk, or technology domains, with deep hands-on experience in data engineering, analytics, or data architecture.
• Demonstrated experience designing and building data pipelines, data models, and analytics architectures, including batch and/or streaming patterns.
• Practical experience partnering with or working on modern data platforms and tools such as Databricks, Redshift, Snowflake, Alteryx, or equivalent technologies.
• Working knowledge of Cybersecurity domains, including data privacy, data protection, and core security concepts (e.g., vulnerabilities, threats, controls, risk).
• Strong coding proficiency (e.g., Python, SQL) with the ability to assess multiple data sources and determine feasibility, data gaps, and engineering approaches to support Cyber use cases.
• Experience collaborating closely with data engineering, platform, and architecture teams to ensure long-term operability and support.