Staff Analytics Engineer

As part of the Data team at Cribl, you will own the evolution of our analytics engineering platform, ensuring our data models are trusted, scalable, and AI-ready.

Analytics Engineering transforms raw data into governed, business-ready models that power analytics, executive reporting, and AI-enabled decision making. As Cribl continues to scale, this role will establish the architecture, standards, and technical practices that enable consistent, trusted analytics across Product, Engineering, Customer, Marketing, GTM, and Finance.

You will serve as a technical leader for analytics engineering, partnering closely with analysts, data engineers, governance, and business stakeholders to improve the quality, consistency, and reliability of our warehouse. While analysts own business logic within their domains, you will own the technical architecture, modeling standards, and engineering practices that allow our analytics platform to scale.

If you enjoy building systems that enable others, care deeply about data quality and developer experience, and want to shape the future of analytics at Cribl, we'd love to talk to you.

• 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.
• We are a remote-first company and work happens across many time-zones - you may be required to occasionally perform duties outside your standard working hours.

• 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, with 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.
• 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.

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