Staff Data Engineer

Cribl is building the telemetry infrastructure for the AI era, partnering with IT and Security teams at major enterprises. The Staff Data Engineer will lead technical direction and architecture for data systems, mentor engineers, and drive the development of the modern data stack.

Responsibilities:

• Serve as a technical lead for the Data Engineering team. Own architecture and design decisions for core data systems and drive reliable, production-grade outcomes
• Partner with stakeholders across functions (Data Analysts, SREs, IT Engineers, and business teams). Clarify requirements, validate outputs, and communicate risks, tradeoffs, and timelines proactively
• Own and operate our self-hosted orchestration platform. Run and evolve our self-managed Prefect deployment and its build/worker system. Own the operational hygiene, upgrade path, and cost/footprint tradeoffs of running orchestration on a hybrid, self-hosted model
• Own Cribl's Snowflake environment. Serve as the subject-matter expert on data modeling, performance, and cost optimization, and own RBAC/permissions design, permissions tooling, and governance
• Build, operate, and monitor the core data tech stack. This covers data pipelines, ingestion services, integrations, and the warehouse, ensuring data is accurate, timely, and trusted, with logging, alerting, and observability as first-class concerns
• Advance the modern data stack. Drive our dbt practice forward, including the in-flight dbt Fusion migration, and own warehouse cost/performance at scale
• Develop cloud-native services and infrastructure on AWS. This includes our ingest/API services (event-driven ingestion, upsert patterns, package reuse) and the IaC that supports them
• Advance our infrastructure-as-code practice (Terraform or similar). Build reusable modules, clean deployment patterns, CI integration, and strong operational hygiene, including self-hosted/hybrid deployment patterns
• Partner with the Data Engineering manager to set technical direction. Shape the platform roadmap, engineering standards, and build-vs-buy/architectural tradeoffs
• Mentor and grow data engineers through code review, pairing, design feedback, and coaching that levels up junior and mid-level engineers
• Support Cribl's growing data science and agentic initiatives. Prepare model-ready datasets, expose features, and integrate AI/LLM workflows into production
• Contribute to secure, compliance-minded engineering practices in collaboration with IT/Security

Requirements:

• Deep expertise in Snowflake: advanced data modeling, query and warehouse performance tuning, cost management, and security/RBAC design. This is the most important qualification for the role
• Demonstrated technical leadership: experience as a tech lead or staff-level engineer setting direction, driving architecture, and delivering complex systems across a team
• Excellent communication and cross-functional collaboration: able to work across data, engineering, and business teams and lead through influence
• A track record of mentoring engineers: growing junior and mid-level engineers through coaching, review, and pairing
• Strong Python and SQL fundamentals, with solid ELT patterns and data modeling experience
• Hands-on experience operating self-hosted / self-managed orchestration. Prefect strongly preferred; equivalent experience running (not just authoring on) Airflow/Dagster on your own infrastructure is acceptable. Must be comfortable owning upgrades, workers, and the operational footprint, not only writing DAGs/flows
• Hands-on AWS and infrastructure-as-code experience: building cloud infrastructure with Terraform (or equivalent) using predictable, maintainable patterns
• Experience building cloud applications or backend services: APIs, ingestion services, and event-driven workflows
• Production-grade engineering practices: logging, alerting, versioning, CI/CD
• Fluency building with AI-assisted development platforms such as Claude Code, with a demonstrated ability to use them to accelerate delivery