Principal Security Data Engineer

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.

Responsibilities

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.

Requirements

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.

Nice to have

Cybersecurity certifications (e.g., CISSP, CISM) and/or data and analytics certifications (or equivalent advanced experience); Demonstrated experience applying advanced analytics techniques (e.g., predictive modeling, risk indicators, trend analysis) to Cybersecurity or technology risk problems; Experience incorporating or experimenting with AI-enabled analytics or development tools (e.g., Claude, AI code assistants, agent-based analytics) in a secure enterprise environment; Leadership experience as a technical lead, architect, or people manager within data, analytics, or Cyber practices; Strong proficiency with data visualization and BI tools (e.g., Tableau, Power BI) to deliver executive-ready, actionable insights; Deep understanding of the Cyber data domain, including the ability to map analytics and outcomes to frameworks such as NIST CSF and familiarity with MITRE ATT&CK; Experience working with enterprise data ecosystems, including data lakes, warehouses, and shared analytics platforms; Proven ability to operate as a consultative thought partner, shaping innovative use cases aligned to top Cyber risks and enterprise priorities; Track record of leading or delivering end-to-end data and analytics initiatives that materially improved Cyber risk management, visibility, or decision-making; Exceptional communication skills with the ability to translate complex technical outputs into business and risk context for senior leaders and non-technical audiences.