Staff Analytics Engineer

Cribl

Analytics Engineer responsible for evolving Cribl's analytics engineering platform to be trusted, scalable, and AI-ready. Partnering with various departments to ensure consistent analytics standards.

Posted 7/21/2026full-timeRemote • California • 🇺🇸 United StatesLead💰 $145,000 - $190,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in analytics engineering, focusing on dbt development, SQL proficiency, and modern cloud data warehouse architecture, particularly with Snowflake. Capable of establishing technical standards and mentoring teams while ensuring reliable, self-service analytics through effective collaboration with analysts.

Highest-signal resume keywords

7+ Years Experience In Analytics EngineeringExpert-Level SQLHands-On Experience With dbt In ProductionStrong Understanding Of Snowflake Performance OptimizationExperience Designing Reusable Semantic Models

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Analytics EngineeringDbt DevelopmentSQLData ModelingCI/CDVersion ControlTestingDocumentationData ContractsGovernance

Soft Skills

Strong Communication SkillsMentoring

Tools & Technologies

SnowflakeAI-Enabled Analytics Platforms

Industry Keywords

Cloud Data Warehouse ArchitectureSemantic LayersMetadata ManagementBusiness Requirements TranslationTechnical Standards

Tech Stack

Tools & technologies

CloudSQL

About the role

Key responsibilities & impact

own the long-term architecture and evolution of Cribl's analytics engineering platform
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
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
work may happen across many time zones

Requirements

What you’ll need

7+ years of experience in analytics engineering, data engineering, or a related technical field
at least 3 years of hands-on experience running dbt in a production environment
expert-level SQL
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
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
experience with semantic layers, metadata management, or AI-enabled analytics platforms
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

Benefits

Comp & perks

health, dental, vision, short-term disability, and life insurance
paid holidays and paid time off
a fertility treatment benefit
401(k)
equity