# Staff Analytics Engineer

**Company:** [Cribl](https://hotfix.jobs/companies/cribl)
**Location:** Remote
**Role:** Data Engineering
**Salary:** $145k – $190k/yr
**Experience:** 7+ years
**Skills:** dbt, SQL, Snowflake, dimensional modeling, semantic layers, metadata management, CI/CD, Data Governance, data lineage, analytics engineering
**Posted:** 2026-07-21

> Own the architecture and standards for Cribl's analytics engineering platform using dbt. Build certified models, semantic layers, and governance practices to power trusted analytics, reporting, and AI decision-making across the business.

## Job Description

## Responsibilities
- Own the long-term architecture and evolution of Cribl's analytics engineering platform, including foundational model stabilization and AI readiness.
- Design, build, and maintain certified dbt models as the authoritative source for business-critical metrics.
- Establish and enforce analytics engineering standards for modeling, testing, documentation, and code review, including review of high-impact dbt changes.
- Design and maintain semantic and metadata layers that enable reliable AI-powered analytics and self-service.
- Partner with analysts to migrate high-value business logic from Omni into governed warehouse models.
- Partner with Data Engineering to improve source reliability, warehouse architecture, and Snowflake performance and cost efficiency.
- Mentor analysts and analytics engineers on dbt development, data modeling, and analytics engineering best practices.

## Requirements
- 7+ years of experience in analytics engineering, data engineering, or a related technical field, including at least 3 years of hands-on experience running dbt in a production environment.
- Expert-level SQL and demonstrated ownership of scalable dimensional models.
- Deep expertise in modern analytics engineering practices, including version control, testing, CI/CD, documentation, data contracts, lineage, and governance.
- Experience designing reusable semantic models, certified metrics, and warehouse architectures that enable self-service analytics across multiple business domains.
- Strong understanding of Snowflake performance optimization and modern cloud data warehouse architecture.
- Demonstrated ability to establish technical standards, influence engineering practices without formal authority, and improve platform reliability while reducing technical debt.
- Proven ability to partner closely with analysts to translate business requirements into scalable, maintainable warehouse models.
- Strong communication skills with the ability to explain complex technical concepts and tradeoffs to both technical and business audiences.

## Nice-to-Haves
- Experience with semantic layers, metadata management, or AI-enabled analytics platforms.

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