Staff Analytics Engineer
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.
About the job
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.
Skills
dbt, SQL, Snowflake, Dimensional Modeling, Semantic Layers, Metadata Management, CI/CD, Data Governance, Data Lineage, Analytics Engineering
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