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# Product Analytics Engineer

- **Company:** HORIZON 3, LLC
- **Location:** Chicago, IL, United States (Remote available)
- **Experience:** Expert
- **Salary:** $143,000.0 - $187,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Information Systems, Continuous Integration, Information Engineering, Data Governance, Data Visualization, Operational Databases, Power BI, Tableau (Software), Technical Data Management Systems, Usage Analysis, Google Cloud, Sql Optimization, Git Flow, Information Technology, Looker Analytics, Data Pipelines
- **Published:** October 6, 2026
- **Apply:** https://www.builtincolorado.com/job/senior-product-analytics-engineer/11514716?handler=ApplyRedirect

## About the Role

* Bachelor's degree or equivalent in Computer Science, Engineering, Information Systems, or a related field
 * 7+ years of experience spanning data engineering and analytics, ideally in a role bridging both
 * Advanced SQL and hands-on experience with dbt
 * Hands-on experience building both production data pipelines and stakeholder-facing analytics/dashboards
 * Experience translating ambiguous business requirements into governed, well-documented data models

Preferred Education/Experience:

 * Proven experience partnering directly with product/engineering teams on instrumentation and source data design - not just consuming what's handed downstream
 * Experience standing up or contributing to a data governance or metrics-certification process
 * Familiarity with BI/visualization tools (Tableau, Looker, Power BI)
 * Experience with cloud platforms (AWS, GCP, Azure)
 * Experience with Git-based workflows and CI/CD for data pipelines
 * Regular use of AI in coding/development workflows (agents, memory files, copilots)

## Description

* 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, not just what's convenient downstream.
 * Partner with the new Data Governance function: support taxonomy definition work, 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 with confidence without needing a SQL expert in the room every time.
 * Own data quality monitoring and anomaly detection for product usage data specifically, partnering with Data Engineering when issues trace back to pipeline or platform-level causes.
 * Contribute analytics engineering support to the expansion/upsell cohort work, building the pipelines and models that turn usage thresholds, feature adoption, and seat utilization into certified, production-grade metrics.
 * Collaborate with data people across the company to help define analytics standards and tooling enablement and then ensure Product Analytics' practices align with the company-wide methodology as it matures.
 * Present technical data concepts and their business implications clearly to both engineering and non-technical stakeholders, including leadership.
 * Drive engineering best practices within the Product Analytics team's data assets.

What You'll Bring:

 * A builder's mindset for data - you enjoy shaping how product data is modeled at the source, not just querying what already exists
 * Comfort moving fluidly between technical and business conversations
 * A governance driven approach to data. You default to documenting, defining, and getting alignment on what a metric means, rather than shipping something that "mostly works"
 * Bias toward self-service, you build data models assuming someone else will need to understand and trust them without you in the room
 * Curiosity and ownership when something looks off in the data
 * Adaptability in a fast-paced, evolving data environment and comfortable building foundational structure while priorities and requirements are still taking shape