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Horizon3.ai

Autonomous pentesting SaaS for vulnerabilities

Federal Senior Product Analytics Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USARemote

About the Job

Requirements

  • A bachelor's degree or equivalent in Computer Science, Engineering, Information Systems, or a related field.
  • Seven or more years of experience spanning data engineering and analytics, ideally in a role bridging both.
  • Advanced SQL and hands-on experience with dbt and/or government-approved equivalents.
  • Hands-on experience building production data pipelines and stakeholder-facing analytics or dashboards.
  • Experience translating ambiguous business requirements into governed, well-documented data models.
  • A builder's mindset for data, with interest in shaping product data models at the source.
  • Comfort moving between technical and business conversations.
  • A governance-driven approach, including documenting, defining, and aligning on metric definitions.
  • A bias toward self-service, building models that others can understand and trust.
  • Curiosity and ownership when data appears incorrect.
  • Adaptability in a fast-paced, evolving data environment, including building foundational structure as priorities and requirements evolve.

Responsibilities

  • Design, build, and maintain scalable ELT pipelines and data models that translate raw product usage and event telemetry into trusted, well-documented analytics assets.
  • Partner with Product Engineering on the canonical product data layer, ensuring product usage cohorts and behavioral signal definitions are built on accurate, governed source data.
  • Partner with Data Governance on taxonomy definition, prepare metrics for certification, and maintain documentation to governance standards, including AI/machine-readable structure.
  • Build and own the semantic layer and self-service data models for Product Analytics so internal stakeholders can query confidently without requiring a SQL expert.
  • Own data quality monitoring and anomaly detection for product usage data, partnering with Data Engineering when issues trace to pipeline or platform-level causes.
  • Support expansion and upsell cohort work by building pipelines and models that turn usage thresholds, feature adoption, and seat utilization into certified, production-grade metrics.
  • Collaborate with data practitioners across the company to define analytics standards and tooling enablement and align Product Analytics practices with the company-wide methodology.
  • Present technical data concepts and business implications to engineering, non-technical stakeholders, and leadership.
  • Drive engineering best practices within the Product Analytics team's data assets.
  • Ensure new tooling and data flows comply with applicable U.S. Federal frameworks, including FedRAMP and DoW IL4/5, and support growth into classified workloads.

Desired Qualifications

  • Proven experience partnering directly with product or engineering teams on instrumentation and source data design, rather than only consuming downstream data.
  • Experience establishing or contributing to data governance or metrics-certification processes.
  • Familiarity with business intelligence and visualization tools such as Tableau, Looker, or Power BI.
  • Experience with cloud platforms such as AWS, GCP, or Azure.
  • Experience with Git-based workflows and CI/CD for data pipelines.
  • Regular use of AI in coding and development workflows, including agents, memory files, or copilots.
  • Experience operating within federally compliant organizations and systems at FedRAMP, IL4/5, and classified-environment levels.

About the Company

Horizon3.ai

Autonomous pentesting SaaS for vulnerabilities

Horizon3.ai specializes in cybersecurity by offering NodeZero, a SaaS platform for autonomous penetration testing. It lets enterprises simulate cyber-attacks on their own systems in a self-service way, without installing persistent agents, and without ongoing human intervention. The platform works by running security-focused attack simulations in production environments to uncover exploitable vulnerabilities and weak controls, delivering vulnerability assessments, threat simulations, and compliance-oriented testing through tiered subscription plans. This approach differentiates Horizon3.ai from traditional pentesting by being self-service, agentless, and safe to run in live environments, while targeting enterprise customers that must meet standards such as PCI DSS. The company aims to help organizations improve their security posture, prioritize remediation, and maintain regulatory compliance by continuously identifying and fixing weaknesses.

Company size
501-1,000
Company stage
Series E
Total funding
$437M
Headquarters
San Francisco, CA
Founded
2019

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