Staff Analytics Engineer

Role Overview

As a Staff Analytics Engineer at Cribl (remote-first), you will own the evolution of the company’s analytics engineering platform. You’ll ensure data models are trusted, scalable, and AI-ready, and you’ll set the architecture and standards that enable consistent analytics across teams.

Key Missions

• Own the evolution of Cribl’s analytics engineering platform, ensuring data models are trusted, scalable, and AI-ready.
• Define architecture, standards, and technical practices for consistent trusted analytics across departments.
• Design, build, and maintain certified dbt models as the authoritative source for business-critical metrics.

Responsibilities (What you’ll do)

• Act as a technical leader for analytics engineering.
• Partner with analysts, data engineers, governance, and business stakeholders to translate business needs into scalable warehouse models.
• Establish and drive analytics engineering practices that improve platform reliability and reduce technical debt.

Requirements

• 7+ years in analytics engineering, data engineering, or a related field.
• 3+ years hands-on running dbt in production.
• Expert-level SQL and demonstrated ownership of scalable dimensional models.
• Strong understanding of Snowflake performance optimization and modern cloud data warehouse architecture.
• Deep expertise in modern analytics engineering practices, including:
  • Version control
  • Testing
  • CI/CD
  • Documentation
  • Data contracts
  • Lineage
  • Governance
• Experience building reusable semantic models, certified metrics, and warehouse architectures that support self-service analytics across domains.
• Strong communication skills; able to explain complex technical concepts and tradeoffs to technical and business audiences.

Nice-to-Haves

• Experience with semantic layers, metadata management, or AI-enabled analytics platforms.
• Proven ability to influence engineering practices without formal authority.