Data Analytics Engineer

Cybersecurity Data Engineer

Key responsibilities include:

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 frontline 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.

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.

Preferred qualifications:

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.