Federal Senior Product Analytics Engineer

Horizon3

1d•$142,718 - $186,560

About The Position

Horizon3 is hiring a Federal Senior Product Analytics Engineer to help advance its company-wide data strategy, making data trusted, governed, and useful for revenue growth, with a focus on the Federal business and platforms. This role connects product data modeling and engineering with trustworthy, accessible analytics. The engineer will work with Data Engineering on the canonical product data layer, Data Governance on metric certification and documentation, and Product analysts and product managers on self-service data products. The position combines hands-on analytics engineering with cross-functional partnership. It involves building and maintaining ELT pipelines, data models, semantic layers, self-service analytics assets, and data quality monitoring; shaping product telemetry and source data definitions; and translating business questions into governed, documented metrics. The role also supports expansion and upsell cohort analytics and ensures new tooling and data flows comply with relevant U.S. Federal frameworks, including FedRAMP and DoW IL4/5, with growth into classified workloads. Horizon3 is a remote cybersecurity company whose NodeZero platform provides autonomous penetration testing and assessment operations for organizations ranging from educational institutions to government agencies and large enterprises. The company describes its culture as collaborative, respectful, ownership-oriented, and focused on results. The role offers competitive compensation, equity for full-time employees, and benefits including health, vision, and dental insurance, flexible vacation, and parental leave.

Requirements

Bachelor's degree or equivalent in Computer Science, Engineering, Information Systems, or a related field.
At least 7 years of experience spanning data engineering and analytics, ideally in a role bridging both.
Advanced SQL skills 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, including an interest in shaping product data models at the source rather than only querying existing data.
Ability to move fluidly between technical and business conversations.
A governance-driven approach, including documenting, defining, and aligning on metric meanings.
A self-service mindset and ability to build models that others can understand and trust independently.
Curiosity and ownership when investigating data anomalies.
Adaptability in a fast-paced, evolving data environment, including building foundational structure while priorities and requirements develop.

Nice To Haves

Experience partnering directly with product or engineering teams on instrumentation and source data design.
Experience establishing or contributing to a data governance or metrics-certification process.
Familiarity with BI or 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 or development workflows, such as agents, memory files, or copilots.
Experience working within federally compliant organizations and systems at FedRAMP or IL4/5 levels, or in classified environments.

Responsibilities

Design, build, and maintain scalable ELT pipelines and data models that turn raw product usage and event telemetry into trusted, documented analytics assets.
Partner with Product Engineering on the canonical product data layer, ensuring product usage cohorts and behavioral signal definitions use accurate, governed source data.
Partner with Data Governance on taxonomy definitions, metric certification, and documentation that meets governance standards, including AI/machine-readable structure.
Build and own the semantic layer and self-service data models for Product Analytics so stakeholders can query data confidently without relying on a SQL expert.
Own data quality monitoring and anomaly detection for product usage data, partnering with Data Engineering when issues originate in pipelines or platforms.
Support expansion and upsell cohort work by building pipelines and models that convert usage thresholds, feature adoption, and seat utilization into certified, production-grade metrics.
Collaborate with data professionals across the company to define analytics standards and tooling enablement, and align Product Analytics practices with the developing company-wide methodology.
Explain technical data concepts and their business implications clearly to engineering and non-technical stakeholders, including leadership.
Drive engineering best practices within Product Analytics data assets.
Ensure new tooling and data flows comply with appropriate U.S. Federal frameworks, including FedRAMP and DoW IL4/5, and support growth into classified workloads.